Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links.

In recent years, deep learning approaches have gained significant attention in predicting brain disorders using neuroimaging data. However, conventional methods often rely on single-modality data and supervised models, which provide only a limited perspective of the intricacies of the highly complex brain. Moreover, the scarcity of accurate diagnostic labels in clinical settings hinders the applicability of the supervised models.

Feasibility of common, enjoyable game play for assessing daily cognitive functioning in older adults.

Background: Frequent digital monitoring of cognition is a promising approach for assessing endpoints in prevention and treatment trials of Alzheimer's disease and related dementias (ADRD). This study evaluated the feasibility of the MIND GamePack for recurrent semi-passive assessment of cognition across a longitudinal interval.

Methods: The MIND GamePack consists of four iPad-based games selected to be both familiar and enjoyable: Word Scramble, Block Drop, FreeCell, and Memory Match.

Identifying canonical and replicable multi-scale intrinsic connectivity networks in 100k+ resting-state fMRI datasets.

Despite the known benefits of data-driven approaches, the lack of approaches for identifying functional neuroimaging patterns that capture both individual variations and inter-subject correspondence limits the clinical utility of rsfMRI and its application to single-subject analyses. Here, using rsfMRI data from over 100k individuals across private and public datasets, we identify replicable multi-spatial-scale canonical intrinsic connectivity network (ICN) templates via the use of multi-model-order independent component analysis (ICA). We also study the feasibility of estimating subject-specific ICNs via spatially constrained ICA.

Changes in patterns of age-related network connectivity are associated with risk for schizophrenia.

Alterations in fMRI-based brain functional network connectivity (FNC) are associated with schizophrenia (SCZ) and the genetic risk or subthreshold clinical symptoms preceding the onset of SCZ, which often occurs in early adulthood. Thus, age-sensitive FNC changes may be relevant to SCZ risk-related FNC. We used independent component analysis to estimate FNC from childhood to adulthood in 9,236 individuals.

Multimodal data fusion of cortical-subcortical morphology and functional network connectivity in psychotic spectrum disorder.

Multiple authors have noted overlapping symptoms and alterations across clinical, anatomical, and functional brain features in schizophrenia (SZ), schizoaffective disorder (SZA), and bipolar disorder (BPI). However, regarding brain features, few studies have approached this line of inquiry using analytical techniques optimally designed to extract the shared features across anatomical and functional information in a simultaneous manner. Univariate studies of anatomical or functional alterations across these disorders can be limited and run the risk of omitting small but potentially crucial overlapping or joint neuroanatomical (e.

Individualized spatial network predictions using Siamese convolutional neural networks: A resting-state fMRI study of over 11,000 unaffected individuals.

Individuals can be characterized in a population according to their brain measurements and activity, given the inter-subject variability in brain anatomy, structure-function relationships, or life experience. Many neuroimaging studies have demonstrated the potential of functional network connectivity patterns estimated from resting functional magnetic resonance imaging (fMRI) to discriminate groups and predict information about individual subjects. However, the predictive signal present in the spatial heterogeneity of brain connectivity networks is yet to be extensively studied.

Federated Analysis of Neuroimaging Data: A Review of the Field.

The field of neuroimaging has embraced sharing data to collaboratively advance our understanding of the brain. However, data sharing, especially across sites with large amounts of protected health information (PHI), can be cumbersome and time intensive. Recently, there has been a greater push towards collaborative frameworks that enable large-scale federated analysis of neuroimaging data without the data having to leave its original location.

Modular and state-relevant functional network connectivity in high-frequency eyes open vs eyes closed resting fMRI data.

Background: Resting-state fMRI (rs-fMRI) is employed to assess "functional connections" of signal between brain regions. However, multiple rs-fMRI paradigms and data-filtering strategies have been used, highlighting the need to explore BOLD signal across the spectrum. Rs-fMRI data is typically filtered at frequencies ranging between 0.

Common and unique multimodal covarying patterns in autism spectrum disorder subtypes.

Background: The heterogeneity inherent in autism spectrum disorder (ASD) presents a substantial challenge to diagnosis and precision treatment. Heterogeneity across biological etiologies, genetics, neural systems, neurocognitive attributes and clinical subtypes or phenotypes has been observed across individuals with ASD.

Methods: In this study, we aim to investigate the heterogeneity in ASD from a multimodal brain imaging perspective.

The relevance of transdiagnostic shared networks to the severity of symptoms and cognitive deficits in schizophrenia: a multimodal brain imaging fusion study.

Schizophrenia (SZ) is frequently concurrent with substance use, depressive symptoms, social communication and attention deficits. However, the relationship between common brain networks (e.g.

Reproducibility of whole-brain temperature mapping and metabolite quantification using proton magnetic resonance spectroscopy.

Assessing brain temperature can provide important information about disease processes (e.g., stroke, trauma) and therapeutic effects (e.

Covarying structural alterations in laterality of the temporal lobe in schizophrenia: A case for source-based laterality.

The human brain is asymmetrically lateralized for certain functions (such as language processing) to regions in one hemisphere relative to the other. Asymmetries are measured with a laterality index (LI). However, traditional LI measures are limited by a lack of consensus on metrics used for its calculation.

The spatial chronnectome reveals a dynamic interplay between functional segregation and integration.

The brain is highly dynamic, reorganizing its activity at different interacting spatial and temporal scales, including variation within and between brain networks. The chronnectome is a model of the brain in which nodal activity and connectivity patterns change in fundamental and recurring ways over time. Most literature assumes fixed spatial nodes/networks, ignoring the possibility that spatial nodes/networks may vary in time.

Spatial dynamics within and between brain functional domains: A hierarchical approach to study time-varying brain function.

The analysis of time-varying activity and connectivity patterns (i.e., the chronnectome) using resting-state magnetic resonance imaging has become an important part of ongoing neuroscience discussions.

Hemispheric differences in language processing in autism spectrum disorders: A meta-analysis of neuroimaging studies.

Language impairments, a hallmark feature of autism spectrum disorders (ASD), have been related to neuroanatomical and functional abnormalities. Abnormal lateralization of the functional language network, increased reliance on visual processing areas, and increased posterior brain activation have all been reported in ASD and proposed as explanatory models of language difficulties. Nevertheless, inconsistent findings across studies have prevented a comprehensive characterization of the functional language network in ASD.

Neuroanatomical and neurofunctional markers of social cognition in autism spectrum disorder.

Social impairments in autism spectrum disorder (ASD), a hallmark feature of its diagnosis, may underlie specific neural signatures that can aid in differentiating between those with and without ASD. To assess common and consistent patterns of differences in brain responses underlying social cognition in ASD, this study applied an activation likelihood estimation (ALE) meta-analysis to results from 50 neuroimaging studies of social cognition in children and adults with ASD. In addition, the group ALE clusters of activation obtained from this was used as a social brain mask to perform surface-based cortical morphometry (SBM) in an empirical structural MRI dataset collected from 55 ASD and 60 typically developing (TD) control participants.

Novel verbal fluency scores and structural brain imaging for prediction of cognitive outcome in mild cognitive impairment.

Introduction: The objective of this study was to assess the utility of novel verbal fluency scores for predicting conversion from mild cognitive impairment (MCI) to clinical Alzheimer's disease (AD).

Method: Verbal fluency lists (animals, vegetables, F, A, and S) from 107 MCI patients and 51 cognitively normal controls were transcribed into electronic text files and automatically scored with traditional raw scores and five types of novel scores computed using methods from machine learning and natural language processing. Additional scores were derived from structural MRI scans: region of interest measures of hippocampal and ventricular volumes and gray matter scores derived from performing ICA on measures of cortical thickness.

Anatomical likelihood estimation meta-analysis of grey and white matter anomalies in autism spectrum disorders.

Autism spectrum disorders (ASD) are characterized by impairments in social communication and restrictive, repetitive behaviors. While behavioral symptoms are well-documented, investigations into the neurobiological underpinnings of ASD have not resulted in firm biomarkers. Variability in findings across structural neuroimaging studies has contributed to difficulty in reliably characterizing the brain morphology of individuals with ASD.

Multimodal neuroimaging based classification of autism spectrum disorder using anatomical, neurochemical, and white matter correlates.

Neuroimaging techniques, such as fMRI, structural MRI, diffusion tensor imaging (DTI), and proton magnetic resonance spectroscopy (1H-MRS) have uncovered evidence for widespread functional and anatomical brain abnormalities in autism spectrum disorder (ASD) suggesting it to be a system-wide neural systems disorder. Nevertheless, most previous studies have focused on examining one index of neuropathology through a single neuroimaging modality, and seldom using multiple modalities to examine the same cohort of individuals. The current study aims to bring together multiple brain imaging modalities (structural MRI, DTI, and 1H-MRS) to investigate the neural architecture in the same set of individuals (19 high-functioning adults with ASD and 18 typically developing (TD) peers).

Enhanced parietal cortex activation during location detection in children with autism.

Background: Visuospatial processing has been found to be mediated primarily by two cortical routes, one of which is unique to recognizing objects (occipital-temporal, ventral or "what" pathway) and the other to detecting the location of objects in space (parietal-occipital, dorsal or "where" pathway). Considering previous findings of relative advantage in people with autism in visuospatial processing, this functional MRI study examined the connectivity in the dorsal and ventral pathways in high-functioning children with autism.

Methods: Seventeen high-functioning children and adolescents with autism spectrum disorders (ASD) and 19 age-and-IQ-matched typically developing (TD) participants took part in this study.

Surface-based morphometry of the cortical architecture of autism spectrum disorders: volume, thickness, area, and gyrification.

Structural neuroimaging studies of autism spectrum disorder (ASD) have uncovered widespread neuroanatomical abnormalities, which may have a significant impact on brain function, connectivity, and on behavioral symptoms of autism. The findings of previous structural MRI studies have largely been distributed across several brain areas, with limited consistency. The current study examined neuroanatomical abnormalities by comparing surface-based measures of cortical morphology (CT: cortical thickness, CSA: cortical surface area, CV: cortical volume, and GI: gyrification index) in 55 high-functioning children and adults with ASD to 60 age-and-IQ-matched typically developing (TD) peers.

Latent information in fluency lists predicts functional decline in persons at risk for Alzheimer disease.

Objective: We constructed random forest classifiers employing either the traditional method of scoring semantic fluency word lists or new methods. These classifiers were then compared in terms of their ability to diagnose Alzheimer disease (AD) or to prognosticate among individuals along the continuum from cognitively normal (CN) through mild cognitive impairment (MCI) to AD.

Method: Semantic fluency lists from 44 cognitively normal elderly individuals, 80 MCI patients, and 41 AD patients were transcribed into electronic text files and scored by four methods: traditional raw scores, clustering and switching scores, "generalized" versions of clustering and switching, and a method based on independent components analysis (ICA).

Lexical factors and cerebral regions influencing verbal fluency performance in MCI.

Objective: To evaluate assumptions regarding semantic (noun), verb, and letter fluency in mild cognitive impairment (MCI) and Alzheimer disease (AD) using novel techniques for measuring word similarity in fluency lists and a region of interest (ROI) analysis of gray matter correlates.

Method: Fifty-eight individuals with normal cognition (NC, n=25), MCI (n=23), or AD (n=10) underwent neuropsychological tests, including 10 verbal fluency tasks (three letter tasks [F, A, S], six noun categories [animals, water creatures, fruits and vegetables, tools, vehicles, boats], and verbs). All pairs of words generated by each participant on each task were compared in terms of semantic (meaning), orthographic (spelling), and phonemic (pronunciation) similarity.

G9a/GLP histone lysine dimethyltransferase complex activity in the hippocampus and the entorhinal cortex is required for gene activation and silencing during memory consolidation.

Learning triggers alterations in gene transcription in brain regions such as the hippocampus and the entorhinal cortex (EC) that are necessary for long-term memory (LTM) formation. Here, we identify an essential role for the G9a/G9a-like protein (GLP) lysine dimethyltransferase complex and the histone H3 lysine 9 dimethylation (H3K9me2) marks it catalyzes, in the transcriptional regulation of genes in area CA1 of the rat hippocampus and the EC during memory consolidation. Contextual fear learning increased global levels of H3K9me2 in area CA1 and the EC, with observable changes at the Zif268, DNMT3a, BDNF exon IV, and cFOS gene promoters, which occurred in concert with mRNA expression.