Objectives: Connectionist theories of brain function took hold with the seminal contributions of Norman Geschwind a half century ago. Modern neuroimaging techniques have expanded the scientific interest in the study of brain connectivity to include the intact as well as disordered brain. Methods: In this review, we describe the most common techniques used to measure functional and structural connectivity, including resting state functional MRI, diffusion MRI, and electroencephalography and magnetoencephalography coherence. We also review the most common analytical approaches used for examining brain interconnectivity associated with these various imaging methods. Results: This review presents a critical analysis of the assumptions, as well as methodological limitations, of each imaging and analysis approach. Conclusions: The overall goal of this review is to provide the reader with an introduction to evaluating the scientific methods underlying investigations that probe the human connectome. (JINS, 2016, 22, 105–119)

Objectives: Recent advances in neuroimaging methodologies sensitive to axonal injury have made it possible to assess in vivo the extent of traumatic brain injury (TBI) -related disruption in neural structures and their connections. The objective of this paper is to review studies examining connectivity in TBI with an emphasis on structural and functional MRI methods that have proven to be valuable in uncovering neural abnormalities associated with this condition. Methods: We review studies that have examined white matter integrity in TBI of varying etiology and levels of severity, and consider how findings at different times post-injury may inform underlying mechanisms of post-injury progression and recovery. Moreover, in light of recent advances in neuroimaging methods to study the functional connectivity among brain regions that form integrated networks, we review TBI studies that use resting-state functional connectivity MRI methodology to examine neural networks disrupted by putative axonal injury. Results: The findings suggest that TBI is associated with altered structural and functional connectivity, characterized by decreased integrity of white matter pathways and imbalance and inefficiency of functional networks. These structural and functional alterations are often associated with neurocognitive dysfunction and poor functional outcomes. Conclusions: TBI has a negative impact on distributed brain networks that lead to behavioral disturbance. (JINS, 2016, 22, 120–137)

Objectives: The objective was to review the literature on diffusion tensor imaging as well as resting-state functional magnetic resonance imaging and electroencephalography (EEG) to unveil neuroanatomical and neurophysiological substrates of Alzheimer’s disease (AD) as a brain neural network pathology affecting structural and functional cortical connectivity underlying human cognition. Methods: We reviewed papers registered in PubMed and other scientific repositories on the use of these techniques in amnesic mild cognitive impairment (MCI) and clinically mild AD dementia patients compared to cognitively intact elderly individuals (Controls). Results: Hundreds of peer-reviewed (cross-sectional and longitudinal) papers have shown in patients with MCI and mild AD compared to Controls (1) impairment of callosal (splenium), thalamic, and anterior–posterior white matter bundles; (2) reduced correlation of resting state blood oxygen level-dependent activity across several intrinsic brain circuits including default mode and attention-related networks; and (3) abnormal power and functional coupling of resting state cortical EEG rhythms. Clinical applications of these measures are still limited. Conclusions: Structural and functional (in vivo) cortical connectivity measures represent a reliable marker of cerebral reserve capacity and should be used to predict and monitor the evolution of AD and its relative impact on cognitive domains in pre-clinical, prodromal, and dementia stages of AD. (JINS, 2016, 22, 138–163)

Objectives: Clinical neuroscience is increasingly turning to imaging the human brain for answers to a range of questions and challenges. To date, the majority of studies have focused on the neural basis of current psychiatric symptoms, which can facilitate the identification of neurobiological markers for diagnosis. However, the increasing availability and feasibility of using imaging modalities, such as diffusion imaging and resting-state fMRI, enable longitudinal mapping of brain development. This shift in the field is opening the possibility of identifying predictive markers of risk or prognosis, and also represents a critical missing element for efforts to promote personalized or individualized medicine in psychiatry (i.e., stratified psychiatry). Methods: The present work provides a selective review of potentially high-yield populations for longitudinal examination with MRI, based upon our understanding of risk from epidemiologic studies and initial MRI findings. Results: Our discussion is organized into three topic areas: (1) practical considerations for establishing temporal precedence in psychiatric research; (2) readiness of the field for conducting longitudinal MRI, particularly for neurodevelopmental questions; and (3) illustrations of high-yield populations and time windows for examination that can be used to rapidly generate meaningful and useful data. Particular emphasis is placed on the implementation of time-appropriate, developmentally informed longitudinal designs, capable of facilitating the identification of biomarkers predictive of risk and prognosis. Conclusions: Strategic longitudinal examination of the brain at-risk has the potential to bring the concepts of early intervention and prevention to psychiatry. (JINS, 2016, 22, 164–179)

Objectives: The extended face network contains clusters of neurons that perform distinct functions on facial stimuli. Regions in the posterior ventral visual stream appear to perform basic perceptual functions on faces, while more anterior regions, such as the ventral anterior temporal lobe and amygdala, function to link mnemonic and affective information to faces. Anterior and posterior regions are interconnected by a long-range white matter tracts; however, it is not known if variation in connectivity of these pathways explains cognitive performance. Methods: Here, we used diffusion imaging and deterministic tractography in a cohort of 28 neurologically normal adults ages 18–28 to examine microstructural properties of visual fiber pathways and their relationship to certain mnemonic and affective functions involved in face processing. We investigated how inter-individual variability in two tracts, the inferior longitudinal fasciculus (ILF) and the inferior fronto-occipital fasciculus (IFOF), related to performance on tests of facial emotion recognition and face memory. Results: Results revealed that microstructure of both tracts predicted variability in behavioral performance indexed by both tasks, suggesting that the ILF and IFOF play a role in facilitating our ability to discriminate emotional expressions in faces, as well as to remember unique faces. Variation in a control tract, the uncinate fasciculus, did not predict performance on these tasks. Conclusions: These results corroborate and extend the findings of previous neuropsychology studies investigating the effects of damage to the ILF and IFOF, and demonstrate that differences in face processing abilities are related to white matter microstructure, even in healthy individuals. (JINS, 2016, 22, 180–190)

Objectives: The purpose of this study was to assess whether age-related differences in white matter microstructure are associated with altered task-related connectivity during episodic recognition. Methods: Using functional magnetic resonance imaging and diffusion tensor imaging from 282 cognitively healthy middle-to-late aged adults enrolled in the Wisconsin Registry for Alzheimer’s Prevention, we investigated whether fractional anisotropy (FA) within white matter regions known to decline with age was associated with task-related connectivity within the recognition network. Results: There was a positive relationship between fornix FA and memory performance, both of which negatively correlated with age. Psychophysiological interaction analyses revealed that higher fornix FA was associated with increased task-related connectivity amongst the hippocampus, caudate, precuneus, middle occipital gyrus, and middle frontal gyrus. In addition, better task performance was associated with increased task-related connectivity between the posterior cingulate gyrus, middle frontal gyrus, cuneus, and hippocampus. Conclusions: The findings indicate that age has a negative effect on white matter microstructure, which in turn has a negative impact on memory performance. However, fornix microstructure did not significantly mediate the effect of age on performance. Of interest, dynamic functional connectivity was associated with better memory performance. The results of the psychophysiological interaction analysis further revealed that alterations in fornix microstructure explain–at least in part–connectivity among cortical regions in the recognition memory network. Our results may further elucidate the relationship between structural connectivity, neural function, and cognition. (JINS, 2016, 22, 191–204)

Objectives: Cognitive impairment is common in Parkinson’s disease (PD). Three neurocognitive networks support efficient cognition: the salience network, the default mode network, and the central executive network. The salience network is thought to switch between activating and deactivating the default mode and central executive networks. Anti-correlated interactions between the salience and default mode networks in particular are necessary for efficient cognition. Our previous work demonstrated altered functional coupling between the neurocognitive networks in non-demented individuals with PD compared to age-matched control participants. Here, we aim to identify associations between cognition and functional coupling between these neurocognitive networks in the same group of participants. Methods: We investigated the extent to which intrinsic functional coupling among these neurocognitive networks is related to cognitive performance across three neuropsychological domains: executive functioning, psychomotor speed, and verbal memory. Twenty-four non-demented individuals with mild to moderate PD and 20 control participants were scanned at rest and evaluated on three neuropsychological domains. Results: PD participants were impaired on tests from all three domains compared to control participants. Our imaging results demonstrated that successful cognition across healthy aging and Parkinson’s disease participants was related to anti-correlated coupling between the salience and default mode networks. Individuals with poorer performance scores across groups demonstrated more positive salience network/default-mode network coupling. Conclusions: Successful cognition relies on healthy coupling between the salience and default mode networks, which may become dysfunctional in PD. These results can help inform non-pharmacological interventions (repetitive transcranial magnetic stimulation) targeting these specific networks before they become vulnerable in early stages of Parkinson’s disease. (JINS, 2016, 22, 205–215)

Objectives: Processing speed impairment is the most prevalent cognitive deficit in individuals with multiple sclerosis (MS). However, the neural mechanisms associated with processing speed remain under debate. The current investigation provides a dynamic representation of the functioning of the brain network involved in processing speed by examining effective connectivity pattern during a processing speed task in healthy adults and in MS individuals with and without processing speed impairment. Methods: Group assignment (processing speed impaired vs. intact) was based on participants’ performance on the Symbol Digit Modalities test (Parmenter, Testa, Schretlen, Weinstock-Guttman, & Benedict, 2010). First, brain regions involved in the processing speed task were determined in healthy participants. Time series from these functional regions of interest of each group of participants were then subjected to the effective connectivity analysis (Independent Multiple-Sample Greedy Equivalence Search and Linear, Non-Gaussian Orientation, Fixed Structure algorithms) that showed causal influences of one region on another during task performance. Results: The connectivity pattern of the processing speed impaired group was significantly different from the connectivity pattern of the processing speed intact group and of the healthy control group. Differences in the strength of common connections were also observed. Conclusions: Effective connectivity results reveal that MS individuals with processing speed impairment not only have connections that differ from healthy participants and MS individuals without processing speed impairment, but also have increased strengths of connections. (JINS, 2016, 22, 216–224)

Objectives: There is a well-known association between memory impairment and major depressive disorder (MDD). Additionally, recent studies are also showing resting-state functional magnetic resonance imaging (rsMRI) abnormalities in active and remitted MDD. However, no studies to date have examined both rs connectivity and memory performance in early course remitted MDD, nor the relationship between connectivity and semantically cued episodic memory. Methods: The rsMRI data from two 3.0 Tesla GE scanners were collected from 34 unmedicated young adults with remitted MDD (rMDD) and 23 healthy controls (HCs) between 18 and 23 years of age using bilateral seeds in the hippocampus. Participants also completed a semantically cued list-learning test, and their performance was correlated with hippocampal seed-based rsMRI. Regression models were also used to predict connectivity patterns from memory performance. Results: After correcting for sex, rMDD subjects performed worse than HCs on the total number of words recalled and recognized. rMDD demonstrated significant in-network hypoactivation between the hippocampus and multiple fronto-temporal regions, and multiple extra-network hyperconnectivities between the hippocampus and fronto-parietal regions when compared to HCs. Memory performance negatively predicted connectivity in HCs and positively predicted connectivity in rMDD. Conclusions Even when individuals with a history of MDD are no longer displaying active depressive symptoms, they continue to demonstrate worse memory performance, disruptions in hippocampal connectivity, and a differential relationship between episodic memory and hippocampal connectivity. (JINS, 2016, 22, 225–239)

Objectives: One of the most prominent features of schizophrenia is relatively lower general cognitive ability (GCA). An emerging approach to understanding the roots of variation in GCA relies on network properties of the brain. In this multi-center study, we determined global characteristics of brain networks using graph theory and related these to GCA in healthy controls and individuals with schizophrenia. Methods: Participants (N=116 controls, 80 patients with schizophrenia) were recruited from four sites. GCA was represented by the first principal component of a large battery of neurocognitive tests. Graph metrics were derived from diffusion-weighted imaging. Results: The global metrics of longer characteristic path length and reduced overall connectivity predicted lower GCA across groups, and group differences were noted for both variables. Measures of clustering, efficiency, and modularity did not differ across groups or predict GCA. Follow-up analyses investigated three topological types of connectivity—connections among high degree “rich club” nodes, “feeder” connections to these rich club nodes, and “local” connections not involving the rich club. Rich club and local connectivity predicted performance across groups. In a subsample (N=101 controls, 56 patients), a genetic measure reflecting mutation load, based on rare copy number deletions, was associated with longer characteristic path length. Conclusions: Results highlight the importance of characteristic path lengths and rich club connectivity for GCA and provide no evidence for group differences in the relationships between graph metrics and GCA. (JINS, 2016, 22, 240–249)

Objectives: Behavioral variant frontotemporal dementia (bvFTD) is characterized by early atrophy in the frontotemporoinsular regions. These regions overlap with networks that are engaged in social cognition-executive functions, two hallmarks deficits of bvFTD. We examine (i) whether Network Centrality (a graph theory metric that measures how important a node is in a brain network) in the frontotemporoinsular network is disrupted in bvFTD, and (ii) the level of involvement of this network in social-executive performance. Methods: Patients with probable bvFTD, healthy controls, and frontoinsular stroke patients underwent functional MRI resting-state recordings and completed social-executive behavioral measures. Results: Relative to the controls and the stroke group, the bvFTD patients presented decreased Network Centrality. In addition, this measure was associated with social cognition and executive functions. To test the specificity of these results for the Network Centrality of the frontotemporoinsular network, we assessed the main areas from six resting-state networks. No group differences or behavioral associations were found in these networks. Finally, Network Centrality and behavior distinguished bvFTD patients from the other groups with a high classification rate. Conclusions: bvFTD selectively affects Network Centrality in the frontotemporoinsular network, which is associated with high-level social and executive profile. (JINS, 2016, 22, 250–262)

Objectives: Individuals with chronic traumatic brain injury (TBI) often show detrimental deficits in higher order cognitive functions requiring coordination of multiple brain networks. Although assessing TBI-related deficits in higher order cognition in the context of network dysfunction is promising, few studies have systematically investigated altered interactions among multiple networks in chronic TBI. Method: We characterized disrupted resting-state functional connectivity of the default mode network (DMN), dorsal attention network (DAN), and frontoparietal control network (FPCN) whose interactions are required for internally and externally focused goal-directed cognition in chronic TBI. Specifically, we compared the network interactions of 40 chronic TBI individuals (8 years post-injury on average) with those of 17 healthy individuals matched for gender, age, and years of education. Results: The network-based statistic (NBS) on DMN-DAN-FPCN connectivity of these groups revealed statistically significant (pNBS<.05; |Z|>2.58) reductions in within-DMN, within-FPCN, DMN-DAN, and DMN-FPCN connectivity of the TBI group over healthy controls. Importantly, such disruptions occurred prominently in between-network connectivity. Subsequent analyses further exhibited the disrupted connectivity patterns of the chronic TBI group occurring preferentially in long-range and inter-hemispheric connectivity of DMN-DAN-FPCN. Most importantly, graph-theoretic analysis demonstrated relative reductions in global, local and cost efficiency (p<.05) as a consequence of the network disruption patterns in the TBI group. Conclusion: Our findings suggest that assessing multiple networks-of-interest simultaneously will allow us to better understand deficits in goal-directed cognition and other higher order cognitive phenomena in chronic TBI. Future research will be needed to better understand the behavioral consequences related to these network disruptions. (JINS, 2016, 22, 263–279)