Collecting comprehensive data sets of the same subject has become a standard in neuroscience research and uncovering multivariate relationships among collected data sets have gained significant attentions in recent years. Canonical correlation analysis (CCA) is one of the powerful multivariate tools to jointly investigate relationships among multiple data sets, which can uncover disease or environmental effects in various modalities simultaneously and characterize changes during development, aging, and disease progressions comprehensively. In the past 10 years, despite an increasing number of studies have utilized CCA in multivariate analysis, simple conventional CCA dominates these applications. Multiple CCA‐variant techniques have been proposed to improve the model performance; however, the complicated multivariate formulations and not well‐known capabilities have delayed their wide applications. Therefore, in this study, a comprehensive review of CCA and its variant techniques is provided. Detailed technical formulation with analytical and numerical solutions, current applications in neuroscience research, and advantages and limitations of each CCA‐related technique are discussed. Finally, a general guideline in how to select the most appropriate CCA‐related technique based on the properties of available data sets and particularly targeted neuroscience questions is provided.

ObjectiveTo investigate the topographic arrangement and strength of whole-brain white matter (WM) structural connectivity in patients with early-stage drug-naive Parkinson disease (PD).MethodsWe employed a model-free data-driven approach for computing whole-brain WM topologic arrangement and connectivity strength between brain regions by utilizing diffusion MRI of 70 participants with early-stage drug-naive PD and 41 healthy controls. Subsequently, we generated a novel group-specific WM anatomical network by minimizing variance in anatomical connectivity of each group. Global WM connectivity strength and network measures were computed on this group-specific WM anatomical network and were compared between the groups. We tested correlations of these network measures with clinical measures in PD to assess their pathophysiologic relevance.ResultsPD-relevant cortical and subcortical regions were identified in the novel PD-specific WM anatomical network. Impaired modular organization accompanied by a correlation of network measures with multiple clinical variables in early PD were revealed. Furthermore, disease duration was negatively correlated with global connectivity strength of the PD-specific WM anatomical network.ConclusionBy minimizing variance in anatomical connectivity, this study found the presence of a novel WM structural connectome in early PD that correlated with clinical symptoms, despite the lack of a priori analytic assumptions. This included the novel finding of increased structural connectivity between known PD-relevant brain regions. The current study provides a framework for further investigation of WM structural changes underlying the clinical and pathologic heterogeneity of PD.

Purpose To investigate whether combining multiple magnetic resonance (MR) imaging modalities such as T1-weighted and diffusion-weighted MR imaging could reveal imaging biomarkers associated with cognition in active professional fighters. Materials and Methods Active professional fighters (n = 297; 24 women and 273 men) were recruited at one center. Sixty-two fighters (six women and 56 men) returned for a follow-up examination. Only men were included in the main analysis of the study. On the basis of computerized testing, fighters were separated into the cognitively impaired and nonimpaired groups on the basis of computerized testing. T1-weighted and diffusion-weighted imaging were performed, and volume and cortical thickness, along with diffusion-derived metrics of 20 major white matter tracts were extracted for every subject. A classifier was designed to identify imaging biomarkers related to cognitive impairment and was tested in the follow-up dataset. Results The classifier allowed identification of seven imaging biomarkers related to cognitive impairment in the cohort of active professional fighters. Areas under the curve of 0.76 and 0.69 were obtained at baseline and at follow-up, respectively, with the optimized classifier. The number of years of fighting had a significant (P = 8.8 × 10) negative association with fractional anisotropy of the forceps major (effect size [d] = 0.34) and the inferior longitudinal fasciculus (P = .03; d = 0.17). A significant difference was observed between the impaired and nonimpaired groups in the association of fractional anisotropy in the forceps major with number of fights (P = .03, d = 0.38) and years of fighting (P = 6 × 10, d = 0.63). Fractional anisotropy of the inferior longitudinal fasciculus was positively associated with psychomotor speed (P = .04, d = 0.16) in nonimpaired fighters but no association was observed in impaired fighters. Conclusion Without enforcement of any a priori assumptions on the MR imaging-derived measurements and with a multivariate approach, the study revealed a set of seven imaging biomarkers that were associated with cognition in active male professional fighters. RSNA, 2017 Online supplemental material is available for this article.

Neural complexity and brain entropy (BEN) have gained greater interest in recent years. The dynamics of neural signals and their relations with information processing continue to be investigated through different measures in a variety of noteworthy studies. The BEN of spontaneous neural activity decreases during states of reduced consciousness. This evidence has been showed in primary consciousness states, such as psychedelic states, under the name of “the entropic brain hypothesis.” In this manuscript we propose an extension of this hypothesis to physiological and pathological aging. We review this particular facet of the complexity of the brain, mentioning studies that have investigated BEN in primary consciousness states, and extending this view to the field of neuroaging with a focus on resting-state functional Magnetic Resonance Imaging. We first introduce historic and conceptual ideas about entropy and neural complexity, treating the mindbrain as a complex nonlinear dynamic adaptive system, in light of the free energy principle. Then, we review the studies in this field, analyzing the idea that the aim of the neurocognitive system is to maintain a dynamic state of balance between order and chaos, both in terms of dynamics of neural signals and functional connectivity. In our exploration we will review studies both on acute psychedelic states and more chronic psychotic states and traits, such as those in schizophrenia, in order to show the increase of entropy in those states. Then we extend our exploration to physiological and pathological aging, where BEN is reduced. Finally, we propose an interpretation of these results, defining a general trend of BEN in primary states and cognitive aging.

IntroductionPrevious neuroimaging studies of Parkinson's disease (PD) patients have shown changes in whole-brain functional connectivity networks. Whether connectivity changes can be detected in the early stages (first 3 years) of PD by resting-state functional magnetic resonance imaging (fMRI) remains elusive. Research infrastructure including MRI and analytic capabilities is required to investigate this issue. The National Institutes of Health/National Institute of General Medical Sciences Center for Biomedical Research Excellence awards support infrastructure to advance research goals.MethodsStatic and dynamic functional connectivity analyses were conducted on early stage never-medicated PD subjects (N = 18) and matched healthy controls (N = 18) from the Parkinson's Progression Markers Initiative.ResultsAltered static and altered dynamic functional connectivity patterns were found in early PD resting-state fMRI data. Most static networks (with the exception of the default mode network) had a reduction in frequency and energy in specific low-frequency bands. Changes in dynamic networks in PD were associated with a decreased switching rate of brain states.DiscussionThis study demonstrates that in early PD, resting-state fMRI networks show spatial and temporal differences of fMRI signal characteristics. However, the default mode network was not associated with any measurable changes. Furthermore, by incorporating an optimum window size in a dynamic functional connectivity analysis, we found altered whole-brain temporal features in early PD, showing that PD subjects spend significantly more time than healthy controls in a specific brain state. These findings may help in improving diagnosis of early never-medicated PD patients. These key observations emerged in a Center for Biomedical Research Excellence–supported research environment.

Introduction:The aim of the study was to identify abnormalities of whole-brain network functional organization and their relation to clinical measures in a well-characterized, multi-site cohort of very early-stage, drug-naïve Parkinson's Disease (PD) patients.Methods: Functional-MRI data for 16 healthy controls and 20 very early-stage, drug-naïve patients with PD were obtained from the Parkinson's Progression Markers Initiative database after controlling for strict inclusion/exclusion imaging criteria. Connectivity between regions of interest was estimated using Pearson's correlation between averaged time-series, and subsequently a connectivity matrix was obtained for each subject. These connectivity matrices were then used in an unbiased, whole-brain graph theoretical approach to investigate the functional connectome and its correlation with disease severity in very early PD. Results:The current study revealed altered network topology which correlated with multiple clinical measures in very early drug-naïve PD. Decreased functional segregation and integration (both globally and locally) were evident in PD. Importantly, our results demonstrated that most of the cortical regions hypothesized to be involved early in PD manifested decreased graph *

Lateralization of default mode network (DMN) functioning has been shown to change with age. Similarly, lateralization of frontal lobe function has been shown to decline in age. The impact of amyloid pathology and the progression of Alzheimer’s disease (AD) on resting state lateralization has not been investigated. Due to the preferential involvement of the left hemisphere in verbal tasks, there may be a benefit to higher levels of left-lateralization in the performance of verbal memory tasks. Here we compared functional lateralization of the anterior and posterior DMN between four groups of participants: amyloid negative (Aβ-) and amyloid positive (Aβ+) groups with normal cognition (NC), and Aβ+ groups with mild cognitive impairment (Aβ+MCI) or dementia (Aβ+AD). Differences were evident between groups in posterior DMN; the Aβ-NC group was more left-lateralized than both cognitively impaired Aβ+ groups. There was no difference in anterior DMN. No differences in overall network connectivity between groups were observed, suggesting that the functional lateralization finding is not secondary to general changes in connectivity. Left-lateralization of both networks was associated with better verbal recall performance. Older subjects, overall, had less left functional lateralization of the anterior DMN.

The dynamics of the brain's intrinsic networks have been recently studied using co-activation pattern (CAP) analysis. The CAP method relies on few model assumptions and CAP-based measurements provide quantitative information of network temporal dynamics. One limitation of existing CAP-related methods is that the computed CAPs share considerable spatial overlap that may or may not be functionally distinct relative to specific network dynamics. To more accurately describe network dynamics with spatially distinct CAPs, and to compare network dynamics between different populations, a novel data-driven CAP group analysis method is proposed in this study. In the proposed method, a dominant-CAP (d-CAP) set is synthesized across CAPs from multiple clustering runs for each group with the constraint of low spatial similarities among d-CAPs. Alternating d-CAPs with less overlapping spatial patterns can better capture overall network dynamics. The number of d-CAPs, the temporal fraction and spatial consistency of each d-CAP, and the subject-specific switching probability among all d-CAPs are then calculated for each group and used to compare network dynamics between groups. The spatial dissimilarities among d-CAPs computed with the proposed method were first demonstrated using simulated data. High consistency between simulated ground-truth and computed d-CAPs was achieved, and detailed comparisons between the proposed method and existing CAP-based methods were conducted using simulated data. In an effort to physiologically validate the proposed technique and investigate network dynamics in a relevant brain network disorder, the proposed method was then applied to data from the Parkinson's Progression Markers Initiative (PPMI) database to compare the network dynamics in Parkinson's disease (PD) and normal control (NC) groups. Fewer d-CAPs, skewed distribution of temporal fractions of d-CAPs, and reduced switching probabilities among final d-CAPs were found in most networks in the PD group, as compared to the NC group. Furthermore, an overall negative association between switching probability among d-CAPs and disease severity was observed in most networks in the PD group as well. These results expand upon previous findings from in vivo electrophysiological recording studies in PD. Importantly, this novel analysis also demonstrates that changes in network dynamics can be measured using resting-state fMRI data from subjects with early stage PD.