Neurocircuitry of acupuncture effect on cognitive improvement in patients with mild cognitive impairment using magnetic resonance imaging: a study protocol for a randomized controlled trial.
Suh Hyo-Weon,Kim Jieun,Kwon Ojin,Cho Seung-Hun,Kim Jong Woo,Kwak Hui-Yong,Kim Yunna,Lee Kyung Mi,Chung Sun-Yong,Lee Jun-Hwan
BACKGROUND:Mild cognitive impairment (MCI) is defined as a decline in cognitive state with preservation of activities of daily living. Medications such as donepezil and rivastigmine have been commonly prescribed for MCI, but their use is controversial. Acupuncture has been widely used in Korea and has been shown to improve cognitive function. The aim of this study is to evaluate the efficacy of acupuncture for MCI and investigate the effect of acupuncture on structural and functional brain changes in patients with MCI. METHODS:This study is a randomized, assessor-blinded, sham-controlled trial. Fifty participants with MCI will be randomly assigned to the acupuncture group (n = 25) or sham acupuncture group (n = 25). The acupuncture group will receive acupuncture treatment at nine acupuncture points (GV20, EX-HN1, bilateral LI4, and ST36) twice a week for 12 weeks. The sham acupuncture group will receive sham acupuncture treatment at the same points with non-penetrating sham needles. Both groups will be restricted from all other treatments for the improvement of cognitive function. The primary outcome measure is the Digit Span Test (DST). The secondary outcome measures are the Digit Symbol Substitution Test (DSST), Korean version of Montreal Cognitive Assessment (MoCA-K), Seoul Neuropsychological Screening Battery-II (SNSB-II), Beck Depression Inventory-II (BDI-II), State-Trait Anxiety Inventory (STAI), working memory (WM) task performance score, and structural/functional brain changes. Outcomes will be assessed at screening, baseline, 4 and 8 weeks, and after the end of treatment. We will also observe adverse events. In the statistical analysis, a full analysis set and per-protocol analysis will be performed. DISCUSSION:This randomized clinical trial aims to examine the efficacy of acupuncture treatment for MCI. Neuropsychological tests, psychological inventories for measuring depression and anxiety, and magnetic resonance imaging will be performed to investigate the underlying neurological mechanisms and the association between cognition, emotion, and brain networks following acupuncture treatment. The results of the trial will provide evidence supporting the efficacy of acupuncture and also add to the neurobiological understanding of acupuncture treatment for MCI. TRIAL REGISTRATION:Clinical Research Information Service, KCT0002896 . Registered on 25 May 2018.
The effect of ApoE ε4 on longitudinal brain region-specific glucose metabolism in patients with mild cognitive impairment: a FDG-PET study.
Paranjpe Manish D,Chen Xueqi,Liu Min,Paranjpe Ishan,Leal Jeffrey P,Wang Rongfu,Pomper Martin G,Wong Dean F,Benzinger Tammie L S,Zhou Yun,
While the ApoE ε4 allele is a known risk factor for mild cognitive impairment (MCI) and Alzheimer's disease, brain region specific effects remain elusive. In this study, we investigate whether the ApoE ε4 allele exhibits brain region specific effects in longitudinal glucose uptake among patients with MCI from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Preprocessed FDG PET images, MRIs, and demographic information were downloaded from the ADNI database. An iterative reblurred Van Cittertiteration method was used for partial volume correction (PVC) on all PET images. Structural MRIs were used for PET spatial normalization and region of interest (ROI) definition in standard space. Longitudinal changes in ROI FDG standardized uptake value ratio (SUVR) relative to cerebellum in 24 ApoE ε4 carriers and 24 age-matched ApoE ε4 non-carriers were measured for up to 84-months (median 72 months, SD = 11.2 months) and compared using a generalized linear mixed effects model controlling for gender, education, baseline age, and follow-up period. Additionally, voxelwise analysis was performed by implementing a paired t-test comparing matched baseline and 72 month FDG SUVR images in ApoE carriers and non-carriers separately. Results with PVC were compared with ones from non-PVC based analysis. After applying PVC, the superior fontal, parietal, lateral temporal, medial temporal, caudate, thalamus, and post-cingulate, and amygdala regions had greater longitudinal decreases in FDG uptake in ApoE ε4 carriers with MCI compared to non-carriers with MCI. Similar forebrain and limbic clusters were found through voxelwise analysis. Compared to the PVC based analysis, fewer significant ApoE-associated regions and clusters were found in the non-PVC based PET analysis. Our findings suggest that the ApoE ε4 genotype is associated with a longitudinal decline in glucose uptake in 8 forebrain and limbic brain regions in the context of MCI. In conclusion, this 84-months longitudinal FDG PET study demonstrates a novel ApoE ε4-associated brain-region specific glucose metabolism pattern in patients with MCI. Partial volume correction improved FDG PET quantification.
Predicting and Tracking Short Term Disease Progression in Amnestic Mild Cognitive Impairment Patients with Prodromal Alzheimer's Disease: Structural Brain Biomarkers.
Marizzoni Moira,Ferrari Clarissa,Jovicich Jorge,Albani Diego,Babiloni Claudio,Cavaliere Libera,Didic Mira,Forloni Gianluigi,Galluzzi Samantha,Hoffmann Karl-Titus,Molinuevo José Luis,Nobili Flavio,Parnetti Lucilla,Payoux Pierre,Ribaldi Federica,Rossini Paolo Maria,Schönknecht Peter,Salvatore Marco,Soricelli Andrea,Hensch Tilman,Tsolaki Magda,Visser Pieter Jelle,Wiltfang Jens,Richardson Jill C,Bordet Régis,Blin Olivier,Frisoni Giovanni B,
Journal of Alzheimer's disease : JAD
BACKGROUND:Early Alzheimer's disease (AD) detection using cerebrospinal fluid (CSF) biomarkers has been recommended as enrichment strategy for trials involving mild cognitive impairment (MCI) patients. OBJECTIVE:To model a prodromal AD trial for identifying MRI structural biomarkers to improve subject selection and to be used as surrogate outcomes of disease progression. METHODS:APOE ɛ4 specific CSF Aβ42/P-tau cut-offs were used to identify MCI with prodromal AD (Aβ42/P-tau positive) in the WP5-PharmaCog (E-ADNI) cohort. Linear mixed models were performed 1) with baseline structural biomarker, time, and biomarker×time interaction as factors to predict longitudinal changes in ADAS-cog13, 2) with Aβ42/P-tau status, time, and Aβ42/P-tau status×time interaction as factors to explain the longitudinal changes in MRI measures, and 3) to compute sample size estimation for a trial implemented with the selected biomarkers. RESULTS:Only baseline lateral ventricle volume was able to identify a subgroup of prodromal AD patients who declined faster (interaction, p = 0.003). Lateral ventricle volume and medial temporal lobe measures were the biomarkers most sensitive to disease progression (interaction, p≤0.042). Enrichment through ventricular volume reduced the sample size that a clinical trial would require from 13 to 76%, depending on structural outcome variable. The biomarker needing the lowest sample size was the hippocampal subfield GC-ML-DG (granule cells of molecular layer of the dentate gyrus) (n = 82 per arm to demonstrate a 20% atrophy reduction). CONCLUSION:MRI structural biomarkers can enrich prodromal AD with fast progressors and significantly decrease group size in clinical trials of disease modifying drugs.
Cognitive impairment and structural brain damage in multiple system atrophy-parkinsonian variant.
Caso Francesca,Canu Elisa,Lukic Milica Jecmenica,Petrovic Igor N,Fontana Andrea,Nikolic Ivan,Kostic Vladimir S,Filippi Massimo,Agosta Federica
Journal of neurology
In this multiparametric, cross-sectional study, we aimed to investigate cognitive impairment and brain structural changes in patients with multiple system atrophy (MSA)-parkinsonian variant (MSA-p). Twenty-six MSA-p patients and 19 controls underwent clinical and neuropsychological evaluation and 1.5 T brain MRI scan. Cortical thickness measures and volumes of deep grey matter structures were obtained. A regression analysis correlated MRI metrics with clinical features in MSA-p patients. Almost 46% of MSA-p patients showed a mild cognitive impairment involving mainly attentive-executive and memory domains. Apathy and depression were found in half of MSA-p patients. MSA-p patients showed significant cortical thinning of fronto-temporal-parietal regions and atrophy of periaqueductal grey matter, left cerebellar hemisphere, left pallidum and bilateral putamen, compared to controls. Cortical thinning in temporal regions correlated with global cognitive status and memory impairment. Grey matter cerebellar atrophy correlated with motor deficits. MSA-p patients showed a multidomain cognitive impairment with a prominent cortical damage in anterior more than posterior brain regions and grey matter volume reduction in subcortical structures. Cortical and subcortical structural changes might lead to cognitive dysfunction in MSA-p.
Hippocampal and Clinical Trajectories of Mild Cognitive Impairment with Suspected Non-Alzheimer's Disease Pathology.
Chung Jun Ku,Plitman Eric,Nakajima Shinichiro,Caravaggio Fernando,Iwata Yusuke,Gerretsen Philip,Kim Julia,Takeuchi Hiroyoshi,Shinagawa Shunichiro,Patel Raihaan,Chakravarty M Mallar,Graff-Guerrero Ariel,
Journal of Alzheimer's disease : JAD
Suspected non-Alzheimer's disease pathology (SNAP) characterizes individuals showing neurodegeneration (e.g., hypometabolism) without amyloid-β (Aβ). Findings from previous studies regarding clinical and structural trajectories of SNAP are inconsistent. Using data from the Alzheimer's Disease Neuroimaging Initiative, patients with amnestic mild cognitive impairment (MCI) were categorized into four groups: amyloid positive with hypometabolism (Aβ+ND+), amyloid only (Aβ+ND-), neither amyloid nor hypometabolism (Aβ-ND-), and SNAP (Aβ-ND+). Aβ+ND+(n = 33), Aβ+ND-(n = 32), and Aβ-ND-(n = 36) were matched to SNAP for age, gender, apolipoprotein E4 (apoE4) genotype, and scores on the Montreal Cognitive Assessment. Elderly controls (n = 40) were also matched to SNAP for age, gender, and apoE4 genotype. Longitudinal changes were compared across groups in terms of hippocampal volume, clinical symptoms, daily functioning, and cognitive functioning over a 2-year period. At baseline, no difference in cognition and functioning was observed between SNAP and Aβ+groups. SNAP showed worse clinical symptoms and impaired functioning at baseline compared to Aβ-ND-and controls. Two years of follow-up showed no differences in hippocampal volume changes between SNAP and any of the comparison groups. SNAP showed worse functional deterioration in comparison to Aβ-ND-and controls. However, Aβ+ND+ showed more severe changes in clinical symptoms in comparison to SNAP. Thus, patients with MCI and SNAP showed 1) more severe functional deterioration compared to Aβ-ND-and controls, 2) no differences with Aβ+ND-, and 3) less cognitive deterioration than Aβ+ND+. Future studies should investigate what causes SNAP, which is different from typical AD pathology and biomarker cascades.
Corpus callosum atrophy associated with the degree of cognitive decline in patients with Alzheimer's dementia or mild cognitive impairment: a meta-analysis of the region of interest structural imaging studies.
Wang Xu-Dong,Ren Ming,Zhu Min-Wei,Gao Wen-Peng,Zhang Jun,Shen Hong,Lin Zhi-Guo,Feng Hong-Lin,Zhao Chang-Jiu,Gao Keming
Journal of psychiatric research
Individual structural neuroimaging studies of the corpus callosum (CC) in Alzheimer's disease (AD) and mild cognitive impairment (MCI) with the region of interest (ROI) analysis have yielded inconsistent findings. The aim of this study was to conduct a meta-analysis of structural imaging studies using ROI technique to measure the CC midsagittal area changes in patients with AD or MCI. Databases of PubMed, the Cochrane Library, the ISI Web of Science, and Science Direct from inception to June 2014 were searched with key words "corpus callosum" or "callosal", plus "Alzheimer's disease" or "mild cognitive impairment". Twenty-three studies with 603 patients with AD, 146 with MCI, and 638 healthy controls were included in this meta-analysis. Effect size was used to measure the difference between patients with AD or MCI and healthy controls. Significant callosal atrophy was found in MCI patients with an effect size of -0.36 (95% CI, -0.57 to -0.14; P = 0.001). The degree of the CC atrophy in mild AD was less severe than that in moderate AD with a mean effect size -0.69 (95% CI, -0.89 to -0.49) versus -0.92 (95% CI, -1.16 to -0.69), respectively. Comparing with healthy controls, patients with MCI had atrophy in the anterior portion of the CC (i.e., rostrum and genu). In contrast, patients with AD had atrophy in both anterior and posterior portions (i.e., splenium). These results suggest that callosal atrophy may be related to the degree of cognitive decline in patients with MCI and AD, and it may be used as a biomarker for patients with cognitive deficit even before meeting the criteria for AD.
Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment.
Jacquemont Thomas,De Vico Fallani Fabrizio,Bertrand Anne,Epelbaum Stéphane,Routier Alexandre,Dubois Bruno,Hampel Harald,Durrleman Stanley,Colliot Olivier,
Neurobiology of aging
Alzheimer's disease (AD) is increasingly considered as a disconnection syndrome. Previous studies of the structural connectome in early AD stages have focused on mild cognitive impaired subjects (MCI), considering them as a homogeneous group. We studied 168 subjects from the Alzheimer's Disease Neuroimaging Initiative database (116 MCI and 52 cognitively normal subjects). Biomarker-based stratification using amyloid biomarkers (AV45 PET) and neurodegeneration biomarkers (MRI and FDG PET) led to 4 subgroups based on amyloid positivity (A+/-) and neurodegeneration positivity (N+/-): A-N-, A+N-, A-N+, and A+N+. Using diffusion MRI, we showed that both MCI A-N+ and MCI A+N+ subjects displayed an alteration of the white matter in the fornix and a significant bihemispheric network of decreased connections. These network alterations in MCI A+N+ are stronger and more focal than those of MCI A-N+. Only MCI A+N+ subjects exhibited specific changes in hippocampal connectivity and an AD-like alteration pattern. Our results indicate that the connectome disintegration pattern of MCI subgroups differ with respect to brain amyloid and neurodegeneration. Each of these 2 AD biomarkers induces a connectome alteration that is maximal when they coexist.
A comprehensive visual rating scale of brain magnetic resonance imaging: application in elderly subjects with Alzheimer's disease, mild cognitive impairment, and normal cognition.
Jang Jae-Won,Park So Young,Park Young Ho,Baek Min Jae,Lim Jae-Sung,Youn Young Chul,Kim SangYun
Journal of Alzheimer's disease : JAD
BACKGROUND:Brain magnetic resonance imaging (MRI) shows cerebral structural changes. However, a unified comprehensive visual rating scale (CVRS) has seldom been studied. Thus, we combined brain atrophy and small vessel disease scales and used an MRI template as a CVRS. OBJECTIVE:The aims of this study were to design a simple and reliable CVRS, validate it by investigating cerebral structural changes in clinical groups, and made comparison to the volumetric measurements. METHODS:Elderly subjects (n = 260) with normal cognition (NC, n = 65), mild cognitive impairment (MCI, n = 101), or Alzheimer's disease (AD, n = 94) were evaluated with brain MRI according to the CVRS of brain atrophy and small vessel disease. Validation of the CVRS with structural changes, neuropsychological tests, and volumetric analyses was performed. RESULTS:The CVRS revealed a high intra-rater and inter-rater agreement and it reflected the structural changes of subjects with NC, MCI, and AD better than volumetric measures (CVRS-coronal: F = 13.5, p < 0.001; CVRS-axial: F = 19.9, p < 0.001). The area under the receiver operation curve (aROC) of the CVRS showed higher accuracy than volumetric analyses. (NC versus MCI aROC: CVRS-coronal, 0.777; CVRS-axial, 0.773; MCI versus AD aROC: CVRS-coronal, 0.680; CVRS-axial, 0.681). CONCLUSION:The CVRS can be used clinically to conveniently measure structural changes of brain. It reflected cerebral structural changes of clinical groups and correlated with the age better than volumetric measures.
Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database.
Ledig Christian,Schuh Andreas,Guerrero Ricardo,Heckemann Rolf A,Rueckert Daniel
Magnetic resonance (MR) imaging is a powerful technique for non-invasive in-vivo imaging of the human brain. We employed a recently validated method for robust cross-sectional and longitudinal segmentation of MR brain images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Specifically, we segmented 5074 MR brain images into 138 anatomical regions and extracted time-point specific structural volumes and volume change during follow-up intervals of 12 or 24 months. We assessed the extracted biomarkers by determining their power to predict diagnostic classification and by comparing atrophy rates to published meta-studies. The approach enables comprehensive analysis of structural changes within the whole brain. The discriminative power of individual biomarkers (volumes/atrophy rates) is on par with results published by other groups. We publish all quality-checked brain masks, structural segmentations, and extracted biomarkers along with this article. We further share the methodology for brain extraction (pincram) and segmentation (MALPEM, MALPEM4D) as open source projects with the community. The identified biomarkers hold great potential for deeper analysis, and the validated methodology can readily be applied to other imaging cohorts.
A brain stress test: Cerebral perfusion during memory encoding in mild cognitive impairment.
Xie Long,Dolui Sudipto,Das Sandhitsu R,Stockbower Grace E,Daffner Molly,Rao Hengyi,Yushkevich Paul A,Detre John A,Wolk David A
Arterial spin labeled perfusion magnetic resonance imaging (ASL MRI) provides non-invasive quantification of cerebral blood flow, which can be used as a biomarker of brain function due to the tight coupling between cerebral blood flow (CBF) and brain metabolism. A growing body of literature suggests that regional CBF is altered in neurodegenerative diseases. Here we examined ASL MRI CBF in subjects with amnestic mild cognitive impairment (n = 65) and cognitively normal healthy controls (n = 62), both at rest and during performance of a memory-encoding task. As compared to rest, task-enhanced ASL MRI improved group discrimination, which supports the notion that physiologic measures during a cognitive challenge, or "stress test", may increase the ability to detect subtle functional changes in early disease stages. Further, logistic regression analysis demonstrated that ASL MRI and concomitantly acquired structural MRI provide complementary information of disease status. The current findings support the potential utility of task-enhanced ASL MRI as a biomarker in early Alzheimer's disease.
Monitoring disease progression in mild cognitive impairment: Associations between atrophy patterns, cognition, APOE and amyloid.
Falahati Farshad,Ferreira Daniel,Muehlboeck J-Sebastian,Eriksdotter Maria,Simmons Andrew,Wahlund Lars-Olof,Westman Eric
BACKGROUND:A disease severity index (SI) for Alzheimer's disease (AD) has been proposed that summarizes MRI-derived structural measures into a single score using multivariate data analysis. OBJECTIVES:To longitudinally evaluate the use of the SI to monitor disease progression and predict future progression to AD in mild cognitive impairment (MCI). Further, to investigate the association between longitudinal change in the SI and cognitive impairment, Apolipoprotein E (APOE) genotype as well as the levels of cerebrospinal fluid amyloid-beta 1-42 (Aβ) peptide. METHODS:The dataset included 195 AD, 145 MCI and 228 control subjects with annual follow-up for three years, where 70 MCI subjects progressed to AD (MCI-p). For each subject the SI was generated at baseline and follow-ups using 55 regional cortical thickness and subcortical volumes measures that extracted by the FreeSurfer longitudinal stream. RESULTS:MCI-p subjects had a faster increase of the SI over time ( < 0.001). A higher SI at baseline in MCI-p was related to progression to AD at earlier follow-ups ( < 0.001) and worse cognitive impairment ( < 0.001). AD-like MCI patients with the APOE ε4 allele and abnormal Aβ levels had a faster increase of the SI, independently ( = 0.003 and = 0.004). CONCLUSIONS:Longitudinal changes in the SI reflect structural brain changes and can identify MCI patients at risk of progression to AD. Disease-related brain structural changes are influenced independently by APOE genotype and amyloid pathology. The SI has the potential to be used as a sensitive tool to predict future dementia, monitor disease progression as well as an outcome measure for clinical trials.
Identification of Amnestic Mild Cognitive Impairment Using Multi-Modal Brain Features: A Combined Structural MRI and Diffusion Tensor Imaging Study.
Xie Yunyan,Cui Zaixu,Zhang Zhongmin,Sun Yu,Sheng Can,Li Kuncheng,Gong Gaolang,Han Ying,Jia Jianping
Journal of Alzheimer's disease : JAD
Identifying amnestic mild cognitive impairment (aMCI) is of great clinical importance because aMCI is a putative prodromal stage of Alzheimer's disease. The present study aimed to explore the feasibility of accurately identifying aMCI with a magnetic resonance imaging (MRI) biomarker. We integrated measures of both gray matter (GM) abnormalities derived from structural MRI and white matter (WM) alterations acquired from diffusion tensor imaging at the voxel level across the entire brain. In particular, multi-modal brain features, including GM volume, WM fractional anisotropy, and mean diffusivity, were extracted from a relatively large sample of 64 Han Chinese aMCI patients and 64 matched controls. Then, support vector machine classifiers for GM volume, FA, and MD were fused to distinguish the aMCI patients from the controls. The fused classifier was evaluated with the leave-one-out and the 10-fold cross-validations, and the classifier had an accuracy of 83.59% and an area under the curve of 0.862. The most discriminative regions of GM were mainly located in the medial temporal lobe, temporal lobe, precuneus, cingulate gyrus, parietal lobe, and frontal lobe, whereas the most discriminative regions of WM were mainly located in the corpus callosum, cingulum, corona radiata, frontal lobe, and parietal lobe. Our findings suggest that aMCI is characterized by a distributed pattern of GM abnormalities and WM alterations that represent discriminative power and reflect relevant pathological changes in the brain, and these changes further highlight the advantage of multi-modal feature integration for identifying aMCI.
Structural integrity in subjective cognitive decline, mild cognitive impairment and Alzheimer's disease based on multicenter diffusion tensor imaging.
Brueggen Katharina,Dyrba Martin,Cardenas-Blanco Arturo,Schneider Anja,Fliessbach Klaus,Buerger Katharina,Janowitz Daniel,Peters Oliver,Menne Felix,Priller Josef,Spruth Eike,Wiltfang Jens,Vukovich Ruth,Laske Christoph,Buchmann Martina,Wagner Michael,Röske Sandra,Spottke Annika,Rudolph Janna,Metzger Coraline D,Kilimann Ingo,Dobisch Laura,Düzel Emrah,Jessen Frank,Teipel Stefan J,
Journal of neurology
INTRODUCTION:Subjective cognitive decline (SCD) can represent a preclinical stage of Alzheimer's disease. Diffusion tensor imaging (DTI) could aid an early diagnosis, yet only few monocentric DTI studies in SCD have been conducted, reporting heterogeneous results. We investigated microstructural changes in SCD in a larger, multicentric cohort. METHODS:271 participants with SCD, mild cognitive impairment (MCI) or Alzheimer's dementia (AD) and healthy controls (CON) were included, recruited prospectively at nine centers of the observational DELCODE study. DTI was acquired using identical protocols. Using voxel-based analyses, we investigated fractional anisotropy (FA), mean diffusivity (MD) and mode (MO) in the white matter (WM). Discrimination accuracy was determined by cross-validated elastic-net penalized regression. Center effects were explored using variance analyses. RESULTS:MO and FA were lower in SCD compared to CON in several anterior and posterior WM regions, including the anterior corona radiata, superior and inferior longitudinal fasciculus, cingulum and splenium of the corpus callosum (p < 0.01, uncorrected). MD was higher in the superior and inferior longitudinal fasciculus, cingulum and superior corona radiata (p < 0.01, uncorrected). The cross-validated accuracy for discriminating SCD from CON was 67% (p < 0.01). As expected, the AD and MCI groups had higher MD and lower FA and MO in extensive regions, including the corpus callosum and temporal brain regions. Within these regions, center accounted for 3-15% of the variance. CONCLUSIONS:DTI revealed subtle WM alterations in SCD that were intermediate between those in MCI and CON and may be useful to detect individuals with an increased risk for AD in clinical studies.
A divergent breakdown of neurocognitive networks in Parkinson's Disease mild cognitive impairment.
Aracil-Bolaños Ignacio,Sampedro Frederic,Marín-Lahoz Juan,Horta-Barba Andrea,Martínez-Horta Saül,Botí Mariángeles,Pérez-Pérez Jesús,Bejr-Kasem Helena,Pascual-Sedano Berta,Campolongo Antonia,Izquierdo Cristina,Gironell Alexandre,Gómez-Ansón Beatriz,Kulisevsky Jaime,Pagonabarraga Javier
Human brain mapping
Cognitive decline is a major disabling feature in Parkinson's disease (PD). Multimodal imaging studies have shown functional disruption in neurocognitive networks related to cognitive impairment. However, it remains unknown whether these changes are related to gray matter loss, or whether they outline network vulnerability in the early stages of cognitive impairment. In this work, we intended to assess functional connectivity and graph theoretical measures and their relation to gray matter loss in Parkinson's disease with mild cognitive impairment (PD-MCI). We recruited 53 Parkinson's disease patients and classified them for cognitive impairment using Level-1 Movement Disorders Society-Task Force Criteria. Voxel-based morphometry, functional connectivity and graph theoretical measures were obtained on a 3-Tesla MRI scanner. Loss of gray matter was observed in the default mode network (bilateral precuneus), without a corresponding disruption of functional or graph theoretical properties. However, functional and graph theoretical changes appeared in salience network nodes, without evidence of gray matter loss. Global cognition and executive scores showed a correlation with node degree in the right anterior insula. We also found a correlation between visuospatial scores and right supramarginal gyrus node degree. Our findings highlight the loss of functional connectivity and topological features without structural damage in salience network regions in PD-MCI. They also underline the importance of multimodal hubs in the transition to mild cognitive impairment. This functional disruption in the absence of gray matter atrophy suggests that the salience network is a key vulnerable system at the onset of mild cognitive impairment in PD.
Different Functional and Microstructural Changes Depending on Duration of Mild Cognitive Impairment in Parkinson Disease.
Shin N-Y,Shin Y S,Lee P H,Yoon U,Han S,Kim D J,Lee S-K
AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE:The higher cortical burden of Lewy body and Alzheimer disease-type pathology has been reported to be associated with a faster onset of cognitive impairment of Parkinson disease. So far, there has been a few studies only about the changes of gray matter volume depending on duration of cognitive impairment in Parkinson disease. Therefore, our aim was to evaluate the different patterns of structural and functional changes in Parkinson disease with mild cognitive impairment according to the duration of parkinsonism before mild cognitive impairment. MATERIALS AND METHODS:Fifty-nine patients with Parkinson disease with mild cognitive impairment were classified into 2 groups on the basis of shorter (<1 year, n = 16) and longer (≥1 year, n = 43) durations of parkinsonism before mild cognitive impairment. Fifteen drug-naïve patients with de novo Parkinson disease with intact cognition were included for comparison. Cortical thickness, Tract-Based Spatial Statistics, and seed-based resting-state functional connectivity analyses were performed. Age, sex, years of education, age at onset of parkinsonism, and levodopa-equivalent dose were included as covariates. RESULTS:The group with shorter duration of parkinsonism before mild cognitive impairment showed decreased fractional anisotropy and increased mean and radial diffusivity values in the frontal areas compared with the group with longer duration of parkinsonism before mild cognitive impairment (corrected P < .05). The group with shorter duration of parkinsonism before mild cognitive impairment showed decreased resting-state functional connectivity in the default mode network area when the left or right posterior cingulate was used as a seed, and in the dorsolateral prefrontal areas when the left or right caudate was used as a seed (corrected P < .05). The group with longer duration of parkinsonism before mild cognitive impairment showed decreased resting-state functional connectivity mainly in the medial prefrontal cortex when the left or right posterior cingulate was used as a seed, and in the parieto-occipital areas when the left or right caudate was used as a seed (corrected P < .05). No differences in cortical thickness were found in all group contrasts. CONCLUSIONS:Resting-state functional connectivity and WM alterations might be useful imaging biomarkers for identifying changes in patients with Parkinson disease with mild cognitive impairment according to the duration of parkinsonism before mild cognitive impairment. The functional and microstructural substrates may topographically differ depending on the rate of cognitive decline in these patients.
Changes of brain structural network connection in Parkinson's disease patients with mild cognitive dysfunction: a study based on diffusion tensor imaging.
Wang Wanyi,Mei Mingjin,Gao Yuyuan,Huang Biao,Qiu Yihui,Zhang Yuhu,Wang Limin,Zhao Jiehao,Huang Zhiheng,Wang Lijuan,Nie Kun
Journal of neurology
INTRODUCTION:Previous studies have found that white matter (WM) alterations might be correlated in Parkinson's disease (PD) patients with cognitive impairment. This study aimed to investigate WM structural network connectome alterations in PD patients with mild cognitive impairment (PD-MCI) and assess the relationship between cognitive impairment and structural topological network changes in PD patients. METHODS:All 31 healthy controls (HCs) and 71 PD patients (43 PD-NC and 28 PD-MCI) matched for age, sex and education underwent 3.0 T MRI and diffusion tensor imaging (DTI) scan. Graph theoretical analyses and network-based statistical (NBS) analyses were performed to identify the structural WM networks and subnetwork changes in PD-MCI. RESULTS:PD-MCI patients showed significantly decreased global efficiency (E) and increased shortest path length (L) compared with the HC group. Several nodal efficiencies showed significant differences in multiple brain regions among the three groups. The nodal efficiency of the orbitofrontal part was closely related to the overall cognitive ability and multiple sub-cognitive domains. Moreover, NBS analyses identified eight one-connect subnetworks, three two-connect subnetworks and two multi-connect subnetworks with reduced connectivity that characterizes the WM structural organization in PD-MCI patients. The two multi-connect subnetworks were located on the bilateral lobe, and both were centered on the orbitofrontal part. CONCLUSIONS:This study provided new evidence that PD with cognitive dysfunction is associated with WM structural alterations. The nodal efficiency and sub-network analyses focusing on the orbitofrontal part might provide new ideas to explore the physiological mechanism of PD-MCI.
QTc Prolongation in Patients with Dementia and Mild Cognitive Impairment: Neuropsychological and Brain Imaging Correlations.
Danese Alessandra,Federico Angela,Martini Alice,Mantovani Elisa,Zucchella Chiara,Tagliapietra Matteo,Tamburin Stefano,Cavallaro Tiziana,Marafioti Vincenzo,Monaco Salvatore,Turri Giulia
Journal of Alzheimer's disease : JAD
The QTc interval is the electrocardiographic manifestation of ventricular depolarization and repolarization. This marker is often prolonged in acute and chronic neurological conditions. The cause of the cerebrogenic QT prolongation remains unclear. The aim of the study was to analyze the relation between QTc interval and the degree of cognitive impairment and structural brain imaging changes in patients with dementia and mild cognitive impairment (MCI). To this aim, 269 patients were screened, of whom 61 met one or more exclusion criteria. The remaining 208 patients (56 control subjects, 44 patients with MCI, and 108 with dementia) were recruited. Eighty-five patients using drugs causing prolongation of QT interval were further excluded. The QT interval was measured manually in all 12 leads by a single blinded observer, assuming the longest QT value adjusted for heart rate by using the Bazett's formula. All patients underwent a structural brain imaging and the following measures were obtained: the bicaudate ratio and the periventricular hyperintensity and deep white matter hyperintensity using the modified Fazekas scale. Prolonged QTc interval was prevalent in 1) patients with dementia, especially in those with moderate-severe degree; 2) subjects with impairment of praxis and attention, low functional status, and behavioral symptoms; 3) patients with global and temporal atrophy and with higher scores on the Fazekas or leukoaraiosis scales. Degenerative and vascular processes might play a main role in QTc interval prolongation because of the damage to brain areas involved in the control of the autonomic cardiac nervous system.
Changes of brain structure in Parkinson's disease patients with mild cognitive impairment analyzed via VBM technology.
Gao Yuyuan,Nie Kun,Huang Biao,Mei Mingjin,Guo Manli,Xie Sifen,Huang Zhiheng,Wang Limin,Zhao Jiehao,Zhang Yuhu,Wang Lijuan
OBJECTIVE:To analyze changes in cerebral grey matter volume and white matter density in non-dementia Parkinson's disease patients using voxel-based morphometry (VBM) technology; to investigate features of brain structure changes in Parkinson's disease patients with mild cognitive impairment (PD-MCI), and reveal their intrinsic pathological changes. METHODS:Based on the diagnostic criteria of PD-MCI, 23 PD-MCI patients, 23 Parkinson's disease patients with normal cognition (PD-NC), and 21 age- and gender-matched healthy people were recruited for the study. Scans were performed on all subjects on a 3.0T MR scanner to obtain brain structural magnetic resonance images. Images were preprocessed using the VBM8 tool from SPM8 software package on the Matlab R2008a platform, and data were then analyzed using the SPM statistical software package to compare the differences of grey matter volume and white matter density between groups, and to evaluate the brain structural changes corresponding to the overall cognitive function. RESULTS:Compared to the control group, the PD-NC group suffered from grey matter atrophy, mainly found in the prefrontal lobe, limbic lobe and left temporal gyrus. The PD-MCI group suffered from grey matter atrophy found in the frontal lobe, limbic lobe, basal ganglia and cerebellum. Compared to the PD-NC group, the PD-MCI group suffered from grey matter atrophy found in the left-side middle temporal gyrus, inferior temporal gyrus and frontal lobe. The grey matter regions correlated with MMSE score (mainly memory related) including the right cingulate gyrus and the limbic lobe. The grey matter regions correlated with MoCA score (mainly non-memory related) including the frontal lobe, basal ganglia, parahippocampal gyrus, occipital lobe and the cerebellum. Additionally, overall cognitive function in non-dementia PD was mainly located in the frontal and limbic system, and was dominated by subcortical atrophy. CONCLUSION:Structural changes in PD-MCI patients are associated with overall cognitive function, and the atrophic areas are mainly located in the frontal and limbic system, and are dominated by subcortical atrophy. Moreover, atrophy of limbic lobes is associated with impaired memory, whereas frontal lobe atrophy is associated with executive dysfunction. In addition, the subtle brain structure of the PD early cognitive impairment stage and PD-MCI stage can be detected via VBM technology, and thus, local brain atrophy may be a neuroimaging marker for the early diagnosis of PD-MCI.
Exploring Structural and Functional Brain Changes in Mild Cognitive Impairment: A Whole Brain ALE Meta-Analysis for Multimodal MRI.
Gu Lihua,Zhang Zhijun
ACS chemical neuroscience
BACKGROUND:Unraveling novel biomarkers for mild cognitive impairment (MCI) was highlighted in the prevention and modification of Alzheimer's disease (AD). Inconsistent results for comparison between MCI patients and healthy controls (HC) were obtained from previous neuroimaging studies. METHODS:An activation likelihood estimation (ALE) meta-analysis was made for multimodal neuroimaging in MCI. After initial research and step-by-step exclusions procedures, n = 101 articles (MCI, n = 2681; HC, n = 2941, respectively) were included in the study. RESULTS:It detected MCI related gray matter atrophy in the bilateral medial temporal lobe and white matter abnormality in the left posterior cingulate, parahippocampal gyrus, thalamus, caudate, and bilateral precuneus. It revealed MCI-related decreased resting-state activity in the left superior temporal gyrus, right posterior cingulate/precuneus, and uncus and hyperactivation in the inferior parietal lobule and superior parietal lobule compared to HC. Task-related functional neuroimaging studies indicated MCI-related hypoactivation in the left inferior parietal lobule, right posterior cingulate, and bilateral precuneus and hyperactivation in the left middle frontal gyrus, superior parietal lobule, insula, superior temporal gyrus, and right inferior frontal gyrus. CONCLUSIONS:Via this ALE meta-analysis, we obtained these key regions suffering from different kinds of deficits in MCI. These regional abnormalities in MRI studies might serve as biomarkers for early diagnosis of MCI.
Neural Correlates of Cognitive Impairment in Parkinson's Disease: A Review of Structural MRI Findings.
Hall Julie M,Lewis Simon J G
International review of neurobiology
Cognitive impairment is one of the most salient non-motor symptoms of Parkinson's disease (PD). Cognitive decline poses a significant burden on the patient as well as the caregiver and a better understanding of the underlying pathological processes will aid in directing disease-specific treatment. In recent years, significant progress in the understanding of the underlying mechanisms of cognition in PD has been made using neuroimaging modalities. In this review, we will discuss the evidence for gray matter atrophy and cortical thinning, diffusivity changes and white matter hyperintensities in dementia, mild cognitive impairment and in several cognitive domains. Structural MRI studies have revealed considerable changes in gray and white matter in PD patients with cognitive dysfunction, showing marked atrophy and diffusivity changes in patients with dementia. The neural substrates of mild cognitive impairment in PD are more variable, perhaps reflecting a heterogeneous cohort with patients showing deficits in various cognitive domains. This review further highlights the potential areas of future research avenues in cognitive impairment in PD.
Rho GTPase signaling at the synapse: implications for intellectual disability.
Ba Wei,van der Raadt Jori,Nadif Kasri Nael
Experimental cell research
Intellectual disability (ID) imposes a major medical and social-economical problem in our society. It is defined as a global reduction in cognitive and intellectual abilities, associated with impaired social adaptation. The causes of ID are extremely heterogeneous and include non-genetic and genetic changes. Great progress has been made over recent years towards the identification of ID-related genes, resulting in a list of approximately 450 genes. A prominent neuropathological feature of patients with ID is altered dendritic spine morphogenesis. These structural abnormalities, in part, reflect impaired cytoskeleton remodeling and are associated with synaptic dysfunction. The dynamic, actin-rich nature of dendritic spines points to the Rho GTPase family as a central contributor, since they are key regulators of actin dynamics and organization. It is therefore not surprising that mutations in genes encoding regulators and effectors of the Rho GTPases have been associated with ID. This review will focus on the role of Rho GTPase signaling in synaptic structure/function and ID.
Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer's disease.
Lu Donghuan,Popuri Karteek,Ding Gavin Weiguang,Balachandar Rakesh,Beg Mirza Faisal,
Medical image analysis
Alzheimer's disease (AD) is one of the most common neurodegenerative diseases with a commonly seen prodromal mild cognitive impairment (MCI) phase where memory loss is the main complaint progressively worsening with behavior issues and poor self-care. However, not all individuals clinically diagnosed with MCI progress to AD. A fraction of subjects with MCI either progress to non-AD dementia or remain stable at the MCI stage without progressing to dementia. Although a curative treatment of AD is currently unavailable, it is extremely important to correctly identify the individuals in the MCI phase that will go on to develop AD so that they may benefit from a curative treatment when one becomes available in the near future. At the same time, it would be highly desirable to also correctly identify those in the MCI phase that do not have AD pathology so they may be spared from unnecessary pharmocologic interventions that, at best, may provide them no benefit, and at worse, could further harm them with adverse side-effects. Additionally, it may be easier and simpler to identify the cause of the cognitive impairment in these non-AD cases, and hence proper identification of prodromal AD will be of benefit to these individuals as well. Fluorodeoxy glucose positron emission tomography (FDG-PET) captures the metabolic activity of the brain, and this imaging modality has been reported to identify changes related to AD prior to the onset of structural changes. Prior work on designing classifier using FDG-PET imaging has been promising. Since deep-learning has recently emerged as a powerful tool to mine features and use them for accurate labeling of the group membership of given images, we propose a novel deep-learning framework using FDG-PET metabolism imaging to identify subjects at the MCI stage with presymptomatic AD and discriminate them from other subjects with MCI (non-AD / non-progressive). Our multiscale deep neural network obtained 82.51% accuracy of classification just using measures from a single modality (FDG-PET metabolism data) outperforming other comparable FDG-PET classifiers published in the recent literature.
Early Detection of Alzheimer's Disease Using Magnetic Resonance Imaging: A Novel Approach Combining Convolutional Neural Networks and Ensemble Learning.
Pan Dan,Zeng An,Jia Longfei,Huang Yin,Frizzell Tory,Song Xiaowei
Frontiers in neuroscience
Early detection is critical for effective management of Alzheimer's disease (AD) and screening for mild cognitive impairment (MCI) is common practice. Among several deep-learning techniques that have been applied to assessing structural brain changes on magnetic resonance imaging (MRI), convolutional neural network (CNN) has gained popularity due to its superb efficiency in automated feature learning with the use of a variety of multilayer perceptrons. Meanwhile, ensemble learning (EL) has shown to be beneficial in the robustness of learning-system performance via integrating multiple models. Here, we proposed a classifier ensemble developed by combining CNN and EL, i.e., the CNN-EL approach, to identify subjects with MCI or AD using MRI: i.e., classification between (1) AD and healthy cognition (HC), (2) MCIc (MCI patients who will convert to AD) and HC, and (3) MCIc and MCInc (MCI patients who will not convert to AD). For each binary classification task, a large number of CNN models were trained applying a set of sagittal, coronal, or transverse MRI slices; these CNN models were then integrated into a single ensemble. Performance of the ensemble was evaluated using stratified fivefold cross-validation method for 10 times. The number of the intersection points determined by the most discriminable slices separating two classes in a binary classification task among the sagittal, coronal, and transverse slice sets, transformed into the standard Montreal Neurological Institute (MNI) space, acted as an indicator to assess the ability of a brain region in which the points were located to classify AD. Thus, the brain regions with most intersection points were considered as those mostly contributing to the early diagnosis of AD. The result revealed an accuracy rate of 0.84 ± 0.05, 0.79 ± 0.04, and 0.62 ± 0.06, respectively, for classifying AD vs. HC, MCIc vs. HC, and MCIc vs. MCInc, comparable to previous reports and a 3D deep learning approach (3D-SENet) based on a more state-of-the-art and popular Squeeze-and-Excitation Networks model using channel attention mechanism. Notably, the intersection points accurately located the medial temporal lobe and several other structures of the limbic system, i.e., brain regions known to be struck early in AD. More interestingly, the classifiers disclosed multiple patterned MRI changes in the brain in AD and MCIc, involving these key regions. These results suggest that as a data-driven method, the combined CNN and EL approach can locate the most discriminable brain regions indicated by the trained ensemble model while the generalization ability of the ensemble model was maximized to successfully capture AD-related brain variations early in the disease process; it can also provide new insights into understanding the complex heterogeneity of whole-brain MRI changes in AD. Further research is needed to examine the clinical implication of the finding, capability of the advocated CNN-EL approach to help understand and evaluate an individual subject's disease status, symptom burden and progress, and the generalizability of the advocated CNN-EL approach to locate the most discriminable brain regions in the detection of other brain disorders such as schizophrenia, autism, and severe depression, in a data-driven way.
Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials.
Weiner Michael W,Veitch Dallas P,Aisen Paul S,Beckett Laurel A,Cairns Nigel J,Green Robert C,Harvey Danielle,Jack Clifford R,Jagust William,Morris John C,Petersen Ronald C,Saykin Andrew J,Shaw Leslie M,Toga Arthur W,Trojanowski John Q,
Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION:The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS:We used standard searches to find publications using ADNI data. RESULTS:(1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION:Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.
Genetic studies of quantitative MCI and AD phenotypes in ADNI: Progress, opportunities, and plans.
Saykin Andrew J,Shen Li,Yao Xiaohui,Kim Sungeun,Nho Kwangsik,Risacher Shannon L,Ramanan Vijay K,Foroud Tatiana M,Faber Kelley M,Sarwar Nadeem,Munsie Leanne M,Hu Xiaolan,Soares Holly D,Potkin Steven G,Thompson Paul M,Kauwe John S K,Kaddurah-Daouk Rima,Green Robert C,Toga Arthur W,Weiner Michael W,
Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION:Genetic data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) have been crucial in advancing the understanding of Alzheimer's disease (AD) pathophysiology. Here, we provide an update on sample collection, scientific progress and opportunities, conceptual issues, and future plans. METHODS:Lymphoblastoid cell lines and DNA and RNA samples from blood have been collected and banked, and data and biosamples have been widely disseminated. To date, APOE genotyping, genome-wide association study (GWAS), and whole exome and whole genome sequencing data have been obtained and disseminated. RESULTS:ADNI genetic data have been downloaded thousands of times, and >300 publications have resulted, including reports of large-scale GWAS by consortia to which ADNI contributed. Many of the first applications of quantitative endophenotype association studies used ADNI data, including some of the earliest GWAS and pathway-based studies of biospecimen and imaging biomarkers, as well as memory and other clinical/cognitive variables. Other contributions include some of the first whole exome and whole genome sequencing data sets and reports in healthy controls, mild cognitive impairment, and AD. DISCUSSION:Numerous genetic susceptibility and protective markers for AD and disease biomarkers have been identified and replicated using ADNI data and have heavily implicated immune, mitochondrial, cell cycle/fate, and other biological processes. Early sequencing studies suggest that rare and structural variants are likely to account for significant additional phenotypic variation. Longitudinal analyses of transcriptomic, proteomic, metabolomic, and epigenomic changes will also further elucidate dynamic processes underlying preclinical and prodromal stages of disease. Integration of this unique collection of multiomics data within a systems biology framework will help to separate truly informative markers of early disease mechanisms and potential novel therapeutic targets from the vast background of less relevant biological processes. Fortunately, a broad swath of the scientific community has accepted this grand challenge.
Is the time ripe for new diagnostic criteria of cognitive impairment due to cerebrovascular disease? Consensus report of the International Congress on Vascular Dementia working group.
Perneczky Robert,Tene Oren,Attems Johannes,Giannakopoulos Panteleimon,Ikram M Arfan,Federico Antonio,Sarazin Marie,Middleton Lefkos T
BACKGROUND:Long before Alzheimer's disease was established as the leading cause of dementia in old age, cerebrovascular lesions were known to cause cognitive deterioration and associated disability. Since the middle of the last century, different diagnostic concepts for vascular dementia and related syndromes were put forward, yet no widely accepted diagnostic consensus exists to date. DISCUSSION:Several international efforts, reviewed herein, are ongoing to define cognitive impairment due to cerebrovascular disease in its different stages and subtypes. The role of biomarkers is also being discussed, including cerebrospinal fluid proteins, structural and functional brain imaging, and genetic markers. The influence of risk factors, such as diet, exercise and different comorbidities, is emphasised by population-based research, and lifestyle changes are considered for the treatment and prevention of dementia. CONCLUSION:To improve the diagnosis and management of vascular cognitive impairment, further progress has to be made in understanding the relevant pathomechanisms, including shared mechanisms with Alzheimer's disease; bringing together fragmented research initiatives in coordinated international programs; testing if known risk factors are modifiable in prospective interventional studies; and defining the pre-dementia and pre-clinical stages in line with the concept of mild cognitive impairment due to Alzheimer's disease.
Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning.
Eskildsen Simon F,Coupé Pierrick,García-Lorenzo Daniel,Fonov Vladimir,Pruessner Jens C,Collins D Louis,
Predicting Alzheimer's disease (AD) in individuals with some symptoms of cognitive decline may have great influence on treatment choice and disease progression. Structural magnetic resonance imaging (MRI) has the potential of revealing early signs of neurodegeneration in the human brain and may thus aid in predicting and diagnosing AD. Surface-based cortical thickness measurements from T1-weighted MRI have demonstrated high sensitivity to cortical gray matter changes. In this study we investigated the possibility for using patterns of cortical thickness measurements for predicting AD in subjects with mild cognitive impairment (MCI). We used a novel technique for identifying cortical regions potentially discriminative for separating individuals with MCI who progress to probable AD, from individuals with MCI who do not progress to probable AD. Specific patterns of atrophy were identified at four time periods before diagnosis of probable AD and features were selected as regions of interest within these patterns. The selected regions were used for cortical thickness measurements and applied in a classifier for testing the ability to predict AD at the four stages. In the validation, the test subjects were excluded from the feature selection to obtain unbiased results. The accuracy of the prediction improved as the time to conversion from MCI to AD decreased, from 70% at 3 years before the clinical criteria for AD was met, to 76% at 6 months before AD. By inclusion of test subjects in the feature selection process, the prediction accuracies were artificially inflated to a range of 73% to 81%. Two important results emerge from this study. First, prediction accuracies of conversion from MCI to AD can be improved by learning the atrophy patterns that are specific to the different stages of disease progression. This has the potential to guide the further development of imaging biomarkers in AD. Second, the results show that one needs to be careful when designing training, testing and validation schemes to ensure that datasets used to build the predictive models are not used in testing and validation.
White-matter changes in early and late stages of mild cognitive impairment.
Femir-Gurtuna Banu,Kurt Elif,Ulasoglu-Yildiz Cigdem,Bayram Ali,Yildirim Elif,Soncu-Buyukiscan Ezgi,Bilgic Basar
Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Mild Cognitive Impairment (MCI) is characterized by cognitive deficits that exceed age-related decline, but not interfering with daily living activities. Amnestic type of the disorder (aMCI) is known to have a high risk to progress to Alzheimer's Disease (AD), the most common type of dementia. Identification of very early structural changes in the brain related to the cognitive decline in MCI patients would further contribute to the understanding of the dementias. In the current study, we target to investigate whether the white-matter changes are related to structural changes, as well as the cognitive performance of MCI patients. Forty-nine MCI patients were classified as Early MCI (E-MCI, n = 24) and Late MCI (L-MCI, n = 25) due to their performance on The Free and Cued Selective Reminding Test (FCSRT). Age-Related White-Matter Changes (ARWMC) scale was used to evaluate the white-matter changes in the brain. Volumes of specific brain regions were calculated with the FreeSurfer program. Both group and correlation analyses were conducted to show if there was any association between white-matter hyperintensities (WMHs) and structural changes and cognitive performance. Our results indicate that, L-MCI patients had significantly more WMHs not in all but only in the frontal regions compared to E-MCI patients. Besides, ARWMC scores were not correlated with total hippocampal and white-matter volumes. It can be concluded that WMHs play an important role in MCI and cognitive functions are affected by white-matter changes of MCI patients, especially in the frontal regions.
Disrupted structural and functional brain connectomes in mild cognitive impairment and Alzheimer's disease.
Dai Zhengjia,He Yong
Alzheimer's disease (AD) is the most common type of dementia, comprising an estimated 60-80% of all dementia cases. It is clinically characterized by impairments of memory and other cognitive functions. Previous studies have demonstrated that these impairments are associated with abnormal structural and functional connections among brain regions, leading to a disconnection concept of AD. With the advent of a combination of non-invasive neuroimaging (structural magnetic resonance imaging (MRI), diffusion MRI, and functional MRI) and neurophysiological techniques (electroencephalography and magnetoencephalography) with graph theoretical analysis, recent studies have shown that patients with AD and mild cognitive impairment (MCI), the prodromal stage of AD, exhibit disrupted topological organization in large-scale brain networks (i.e., connectomics) and that this disruption is significantly correlated with the decline of cognitive functions. In this review, we summarize the recent progress of brain connectomics in AD and MCI, focusing on the changes in the topological organization of large-scale structural and functional brain networks using graph theoretical approaches. Based on the two different perspectives of information segregation and integration, the literature reviewed here suggests that AD and MCI are associated with disrupted segregation and integration in brain networks. Thus, these connectomics studies open up a new window for understanding the pathophysiological mechanisms of AD and demonstrate the potential to uncover imaging biomarkers for clinical diagnosis and treatment evaluation for this disease.
Activities, bioavailability, and metabolism of lipids from structural membranes and oils: Promising research on mild cognitive impairment.
Pérez-Gálvez Antonio,Jarén-Galán Manuel,Garrido-Fernández Juan,Calvo M Visitacion,Visioli Francesco,Fontecha Javier
Concomitant with increased lifespan, large segments of the population are experiencing cognitive decline, which might progress to Alzheimer's disease (AD). Currently, there is no cure for AD and, once the neurodegenerative disorders are established, patients use pharmacologic therapy to slow the progression of the symptoms and require appropriate care to manage their condition. The preclinical stage of neural degeneration that progress through mild cognitive impairment (MCI) before the onset of AD is when it might be possible to introduce behavioral changes and pharma-nutritional interventions that modify the risk factors of MCI conversion to AD. Some food components accumulate in brain tissues, where they play essential roles. Among them, polar lipids, omega 3 fatty acids, and carotenoids appear to work additively or synergistically. Therefore, there is an opportunity to formulate nutraceuticals/functional foods to slow the progression of MCI. In this paper, we review the biochemical bases and recent interventions with bioactive lipids-rich formulations. Based on accumulated evidence, we propose that appropriate large-scale trials are warranted.