The left frontal cortex supports reserve in aging by enhancing functional network efficiency

Background: Recent evidence from fMRI studies suggests that functional hubs, i.e. highly connected brain regions, are important for mental health. We found recently that global connectivity of a hub in the left frontal cortex (LFC-connectivity) is associated with relatively preserved memory abilities and higher levels of protective factors (education, IQ) in normal aging and Alzheimer’s disease. These results suggest that LFC-connectivity supports reserve capacity alleviating memory decline. An open question is, however, why LFC-connectivity is beneficial and supports memory function in the face of neurodegeneration. We hypothesized that higher LFCconnectivity is associated with enhanced efficiency in connected major networks involved in episodic memory. We further hypothesized that higher LFC-related network efficiency predicts higher memory abilities. Methods: We assessed fMRI during a face-name association learning task in 26 healthy cognitively normal elderly participants. Using beta-series correlation analysis, we computed task-related LFC-connectivity to key memory networks including the default-mode network (DMN) and dorsal attention network (DAN). Network efficiency within the DMN and DAN was estimated by the graph theoretical small-worldness statistic. We applied linear regression analyses in order to test the association between LFC-connectivity to the DMN/DAN and small-worldness of these networks. Mediation analysis was applied to test LFC-connectivity to the DMN and DAN as a mediator of the association between education and higher DMN and DAN smallworldness. Lastly, we tested network small-worldness as a predictor of memory performance. Results: We found that higher LFC-connectivity to the DMN and DAN during successful memory encoding and recognition was associated with higher small-worldness of those networks. Higher task-related LFC-connectivity mediated the association between education and higher small-worldness in the DMN and DAN. Further, higher small-worldness of these networks predicted better performance in the memory task. Conclusions: The current results suggest that higher education-related LFC-connectivity to key memory networks during a memory task is associated with higher network efficiency and thus enhanced reserve of memory abilities in aging

Functional brain networks and cognitive deficits in Parkinson's disease

Abstract: Graph-theoretical analyses of functional networks obtained with resting-state functional mag-netic resonance imaging (fMRI) have recently proven to be a useful approach for the study of the sub-strates underlying cognitive deficits in different diseases. We used this technique to investigate whethercognitive deficits in Parkinson's disease (PD) are associated with changes in global and local networkmeasures. Thirty-six healthy controls (HC) and 66 PD patients matched for age, sex, and education wereclassified as having mild cognitive impairment (MCI) or not based on performance in the three mainlyaffected cognitive domains in PD: attention/executive, visuospatial/visuoperceptual (VS/VP), anddeclarative memory. Resting-state fMRI and graph theory analyses were used to evaluate network meas-ures. We have found that patients with MCI had connectivity reductions predominantly affecting long-range connections as well as increased local interconnectedness manifested as higher measures of cluster-ing, small-worldness, and modularity. The latter measures also tended to correlate negatively with cogni-tive performance in VS/VP and memory functions. Hub structure was also reorganized: normal hubsdisplayed reduced centrality and degree in MCI PD patients. Our study indicates that the topologicalproperties of brain networks are changed in PD patients with cognitive deficits. Our findings providenovel data regarding the functional substrate of cognitive impairment in PD, which may prove to havevalue as a prognostic marker

Pattern Analysis and Prediction of Mild Cognitive Impairment Using the Conn Toolbox

Alzheimer\u27s is an irreversible neurodegenerative disorder described by dynamic psychological and memory defalcation. It has been accounted for that the pervasiveness of Alzheimer\u27s is to increase by 4 times in a few years, where one in every 75 people will have this disorder. Hence, there is a critical requirement for the analysis of Alzheimer\u27s at its beginning stage to diminish the difficulty of the overall medical complications. The initial state of Alzheimer’s is called Mild cognitive impairment (MCI), and hence it is a decent target for premature diagnosis and treatment of Alzheimer\u27s. This project focuses on coordinating numerous imaging modalities to identify people in danger for MCI. The current advancement of brain network connectivity analysis has led to the identification of neurological issues at an entire connectivity level, thereby providing a new road to the classification of brain-related diseases. Utilizing neuroimage pattern classification and various machine learning techniques, we endeavor to incorporate information from CONN toolbox and resting-state functional magnetic resonance imaging (rs-fMRI) for refining MCI prediction accuracy

Gray matter network differences between behavioral variant frontotemporal dementia and Alzheimer's disease

We set out to study whether single-subject gray matter (GM) networks show disturbances that are specific for Alzheimer's disease (AD; n = 90) or behavioral variant frontotemporal dementia (bvFTD; n = 59), and whether such disturbances would be related to cognitive deficits measured with mini-mental state examination and a neuropsychological battery, using subjective cognitive decline subjects as reference. AD and bvFTD patients had a lower degree, connectivity density, clustering, path length, betweenness centrality, and small world values compared with subjective cognitive decline. AD patients had a lower connectivity density than bvFTD patients (F = 5.79, p = 0.02; mean ± standard deviation bvFTD 16.10 ± 1.19%; mean ± standard deviation AD 15.64 ± 1.02%). Lasso logistic regression showed that connectivity differences between bvFTD and AD were specific to 23 anatomical areas, in terms of local GM volume, degree, and clustering. Lower clustering values and lower degree values were specifically associated with worse mini-mental state examination scores and lower performance on the neuropsychological tests. GM showed disease-specific alterations, when comparing bvFTD with AD patients, and these alterations were associated with cognitive deficits

Complex biomarker discovery in neuroimaging data: Finding a needle in a haystack

AbstractNeuropsychiatric disorders such as schizophrenia, bipolar disorder and Alzheimer's disease are major public health problems. However, despite decades of research, we currently have no validated prognostic or diagnostic tests that can be applied at an individual patient level. Many neuropsychiatric diseases are due to a combination of alterations that occur in a human brain rather than the result of localized lesions. While there is hope that newer imaging technologies such as functional and anatomic connectivity MRI or molecular imaging may offer breakthroughs, the single biomarkers that are discovered using these datasets are limited by their inability to capture the heterogeneity and complexity of most multifactorial brain disorders. Recently, complex biomarkers have been explored to address this limitation using neuroimaging data. In this manuscript we consider the nature of complex biomarkers being investigated in the recent literature and present techniques to find such biomarkers that have been developed in related areas of data mining, statistics, machine learning and bioinformatics

Measures of Resting State EEG Rhythms for Clinical Trials in Alzheimer’s Disease:Recommendations of an Expert Panel

The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer's disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and "interrelatedness" at posterior alpha (8-12Hz) and widespread delta (<4Hz) and theta (4-8Hz) rhythms in relation to disease progression and interventions. The following consensus statements were subscribed: (1) Standardization of instructions to patients, resting state EEG (rsEEG) recording methods, and selection of artifact-free rsEEG periods are needed; (2) power density and "interrelatedness" rsEEG measures (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) at delta, theta, and alpha frequency bands may be use for stratification of AD patients and monitoring of disease progression and intervention; and (3) international multisectoral initiatives are mandatory for regulatory purposes

Investigating White Matter Lesion Load, Intrinsic Functional Connectivity, and Cognitive Abilities in Older Adults

Changes to the while matter of the brain disrupt neural communication between spatially distributed brain regions and are associated with cognitive changes in later life. While approximately 95% of older adults experience these brain changes, not everyone who has significant white matter damage displays cognitive impairment. Few studies have investigated the association between white matter changes and cognition in the context of functional brain network integrity. This study used a data-driven, multivariate analytical model to investigate intrinsic functional connectivity patterns associated with individual variability in white matter lesion load as related to fluid and crystallized intelligence in a sample of healthy older adults (n = 84). Several primary findings were noted. First, a reliable pattern emerged associating whole-brain resting-state functional connectivity with individual variability in measures of white matter lesion load, as indexed by total white matter lesion volume and number of lesions. Secondly, white matter lesion load was associated with increased network disintegration and dedifferentiation. Specifically, lower white matter lesion load was associated with greater within- versus between-network connectivity. Higher white matter lesion load was associated with greater between-network connectivity compared to within. These associations between intrinsic functional connectivity and white matter lesion load were not reliably associated with crystallized and fluid intelligence performance. These results suggest that changes to the white matter of the brain in typically aging older adults are characterized by increased functional brain network dedifferentiation. The findings highlight the role of white matter lesion load in altering the functional network architecture of the brain

Structural and Functional Brain Connectivity in Middle-Aged Carriers of Risk Alleles for Alzheimer\u27s Disease

Single nucleotide polymorphisms (SNPs) in APOE, COMT, BDNF, and KIBRA have been associated with age-related memory performance and executive functioning as well as risk for Alzheimer’s disease (AD). The purpose of the present investigation was to characterize differences in brain functional and structural integrity associated with these SNPs as potential endophenotypes of age-related cognitive decline. I focused my investigation on healthy, cognitively normal middle-aged adults, as disentangling the early effects of healthy versus pathological aging in this group may aid early detection and prevention of AD. The aims of the study were 1) to characterize SNP-related differences in functional connectivity within two resting state networks (RSNs; default mode network [DMN] and executive control network [ECN]) associated with memory and executive functioning, respectively; 2) to identify differences in the white matter (WM) microstructural integrity of tracts underlying these RSNs; and 3) to characterize genotype differences in the graph properties of an integrated functional-structural network. Participants (age 40-60, N = 150) underwent resting state functional magnetic resonance imaging (rs-fMRI), diffusion tensor imaging (DTI), and genotyping. Independent components analysis (ICA) was used to derive RSNs, while probabilistic tractography was performed to characterize tracts connecting RSN subregions. A technique known as functional-by-structural hierarchical (FSH) mapping was used to create the integrated, whole brain functional-structural network, or resting state structural connectome (rsSC). I found that BDNF risk allele carriers had lower functional connectivity within the DMN, while KIBRA risk allele carriers had poorer WM microstructural integrity in tracts underlying the DMN and ECN. In addition to these differences in the connectivity of specific RSNs, I found significant impairments in the global and local topology of the rsSC across all evaluated SNPs. Collectively, these findings suggest that integrating multiple neuroimaging modalities and using graph theoretical analysis may reveal network-level vulnerabilities that may serve as biomarkers of age-related cognitive decline in middle age, decades before the onset of overt cognitive impairment