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. The results show that the subject-level ICN estimations vary as a function of the ICN itself, the data length, and the spatial resolution. In general, large-scale ICNs require less data to achieve specific levels of (within- and between-subject) spatial similarity with their templates. Importantly, increasing data length can reduce an ICN's subject-level specificity, suggesting longer scans may not always be desirable. We also find a positive linear relationship between data length and spatial smoothness (possibly due to averaging over intrinsic dynamics), suggesting studies examining optimized data length should consider spatial smoothness. Finally, consistency in spatial similarity between ICNs estimated using the full data and subsets across different data lengths suggests lower within-subject spatial similarity in shorter data is not wholly defined by lower reliability in ICN estimates, but may be an indication of meaningful brain dynamics which average out as data length increases.

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.g., structural images) and functional features (e.g., fMRI-based features) which may serve as informative clinical indicators of across multiple diagnostic categories. To address this limitation, we paired an unsupervised multimodal canonical correlation analysis (mCCA) together with joint independent component analysis (jICA) to identify linked spatial gray matter (GM), resting-state functional network connectivity (FNC), and white matter fractional anisotropy (FA) features across these diagnostic categories. We then calculated associations between the identified linked features and trans-diagnostic behavioral measures (MATRICs Consensus Cognitive Battery, MCCB). Component number 4 of the 13 identified displayed a statistically significant relationship with overall MCCB scores across GM, resting-state FNC, and FA. These linked modalities of component 4 consisted primarily of positive correlations within subcortical structures including the caudate and putamen in the GM maps with overall MCCB, sparse negative correlations within subcortical and cortical connection tracts (e.g., corticospinal tract, superior longitudinal fasciculus) in the FA maps with overall MCCB, and negative relationships with MCCB values and loading parameters with FNC matrices displaying increased FNC in subcortical-cortical regions with auditory, somatomotor, and visual regions.

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). Random forest classifiers based on raw scores were compared to "augmented" classifiers that incorporated newer scoring methods. Outcome variables included AD diagnosis at baseline, MCI conversion, increase in Clinical Dementia Rating-Sum of Boxes (CDR-SOB) score, or decrease in Financial Capacity Instrument (FCI) score. Receiver operating characteristic (ROC) curves were constructed for each classifier and the area under the curve (AUC) was calculated. We compared AUC between raw and augmented classifiers using Delong's test and assessed validity and reliability of the augmented classifier. RESULTS: Augmented classifiers outperformed classifiers based on raw scores for the outcome measures AD diagnosis (AUC .97 vs. .95), MCI conversion (AUC .91 vs. .77), CDR-SOB increase (AUC .90 vs. .79), and FCI decrease (AUC .89 vs. .72). Measures of validity and stability over time support the use of the method. CONCLUSION: Latent information in semantic fluency word lists is useful for predicting cognitive and functional decline among elderly individuals at increased risk for developing AD. Modern machine learning methods may incorporate latent information to enhance the diagnostic value of semantic fluency raw scores. These methods could yield information valuable for patient care and clinical trial design with a relatively small investment of time and money.

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. We used mixed-effects logistic regression to determine which lexical factors were predictive of word adjacency within the lists. Associations between each fluency raw score and gray matter volumes in sixteen ROIs were identified by means of multiple linear regression. We evaluated causal models for both types of analyses to specify the contributions of diagnosis and various mediator variables to the outcomes of word adjacency and fluency raw score. RESULTS: Semantic similarity between words emerged as the strongest predictor of word adjacency for all fluency tasks, including the letter fluency tasks. Semantic similarity mediated the effect of cognitive impairment on word adjacency only for three fluency tasks employing a biological cue. Orthographic similarity was predictive of word adjacency for the A and S tasks, while phonemic similarity was predictive only for the S task and one semantic task (vehicles). The ROI analysis revealed different patterns of correlations among the various fluency tasks, with the most common associations in the right lower temporal and bilateral dorsal frontal regions. Following correction with gray matter volumes from the opposite hemisphere, significant associations persisted for animals, vehicles, and a composite nouns score in the left inferior frontal gyrus, but for letter A, letter S, and a composite FAS score in the right inferior frontal gyrus. These regressions also revealed a lateralized association of the left subcortical nuclei with all letter fluency scores and fruits and vegetables fluency, and an association of the right lower temporal ROI with letter A, FAS, and verb fluency. Gray matter volume in several bihemispheric ROIs (left dorsal frontal, right lower temporal, right occipital, and bilateral mesial temporal) mediated the relationship between cognitive impairment and fluency for fruits and vegetables. Gray matter volume in the right lower temporal ROI mediated the relationship between cognitive impairment and five fluency raw scores (animals, fruits and vegetables, tools, verbs, and the composite nouns score). CONCLUSION: Semantic memory exerts the strongest influence on word adjacency in letter fluency as well as semantic verbal fluency tasks. Orthography is a stronger influence than pronunciation. All types of fluency task raw scores (letter, noun, and verb) correlate with cerebral regions known to support verbal or nonverbal semantic memory. The findings emphasize the contribution of right hemisphere regions to fluency task performance, particularly for verb and letter fluency. The relationship between diagnosis and semantic fluency performance is mediated by semantic similarity of words and by gray matter volume in the right lower temporal region.