11 CE Credits (credit only available for entire issue). SPECIAL ISSUE: Human Brain Connectivity in the Modern Era. JINS 22:2 (2016)

apa-logo_white_screenThe International Neuropsychological Society is approved by the American Psychological Association to sponsor continuing education for psychologists. The International Neuropsychological Society maintains responsibility for this program and its content.
Educational Objectives
  1. To identify and understand the primary methods by which one can examine human structural and/or functional brain connectivity.
  2. To identify and understand the primary benefits and challenges associated with the different methods used to assess human structural and functional brain connectivity.
  3. To understand relationships between normative variations in structural and/or functional connectivity and behavior.
  4. To identify the populations, brain systems, and behaviors associated with alterations in functional and structural connectivity.
  5. To describe the specificity across clinical domains of associations and the potential mechanisms by which functional and/or structural connectivity influences behavior.
  6. To describe similarities and differences in structural versus functional connectivity abnormalities across clinical populations.

Course Information
Target Audience:Intermediate
Availability:Date Available: 2016-02-17
You may obtain CE for this JINS package at any time.
Offered for CEYes
CostMembers $110
Non-Members $165
Refund PolicyThis JINS package is not eligible for refunds
CE Credits11

Co-Organizers
Deanna M. Barch, Mieke Verfaellie, and Stephen M. Rao

Introduction

Last year (2015) commemorated the 50th anniversary of Norman Geschwind’s seminal papers in Brain on “Disconnexion syndromes in animals and man” (Geschwind, 1965a, 1965b). In the past 50 years, huge advances have occurred in the tools and technologies available for the in vivo assessment of both structural and functional connectivity in the human brain, including diffusion imaging for examining structural brain connectivity, and functional magnetic resonance imaging (fMRI), electroencephalogram (EEG), and magneto-encephalogram (MEG) approaches to understanding functional brain connectivity. This has led to a dramatic increase in our understanding of the core principles of human brain connectivity and their relationship to cognitive, emotional, motor, and sensory function in health, and more recently, in clinical populations.

Facilitated by the availability of novel imaging techniques, this enhanced understanding of brain–behavior relationships reflects a fundamental conceptual shift. Basic and translational research examining task-related brain activation has been remarkably informative in terms of our understanding of the neural substrates of particular cognitive and affective processes and how these may go awry in conditions associated with impaired brain function. However, over time, it has become clear that rarely does any particular cognitive or affective process require only a single brain region, and rarely is any particular form of cognitive or behavioral dysfunction associated with disruption of only a single brain region.

Furthermore, basic neuroscience research has long made it clear that activity in any individual brain region (or any individual neuron!) is the result of inputs from and outputs to different areas of the brain. Such realizations have led to a shift in focus on neural circuits rather than on specific brain regions. More specifically, this shift has been to questions about the relationship between and among different brain regions in producing successful cognitive and affective function in health, and the ways in which abnormalities at the level of circuits contribute to the development and maintenance of specific neuropsychological impairments. The growing work on the role of brain oscillations in coordinating activity within and between neutral networks (Canavier, 2015; Ketz, Jensen, & O’Reilly, 2015; Pittman-Polletta, Kocsis, Vijayan, Whittington, & Kopell, 2015; Uhlhaas & Singer, 2015) is consistent with such hypotheses that localize neuropsychological impairments at the circuit level of function rather than within specific individual brain regions.

Aimed at highlighting this conceptual shift, this special issue has three specific goals. The first is to provide a brief overview of the current methodological and analytic tools available for understanding both normative and dysfunctional human brain connectivity. As outlined in the article by Lowe and colleagues, and to some extent in the article by Hayes and colleagues, we have seen major advances in our ability to image white matter connections in the human brain, including state-of-the art techniques that now allow researchers to follow the path of white matter connections through areas where many different fiber tracts merge, dramatically improving our ability to understand the structural basis of both short and long range communication within brain circuits.

Furthermore, as also described in the article by Lowe and colleagues, the last 30 years have also seen the emergence of methods for studying functional brain connectivity, or the covariance of spontaneous brain activity across brain regions. Originally, the concept of functional connectivity was applied to simultaneous recordings of neuronal spike trains (Gerstein & Perkel, 1969; Gerstein, Perkel, & Subramanian, 1978; Perkel, Gerstein, & Moore, 1967), which are thought to contribute to the functional connectivity observed in humans using non-invasive neuroimaging methods. A main inference of functional connectivity is that, if two regions have highly correlated neuronal activity (i.e., have high functional connectivity), then they are more likely to engage in a common set of processing mechanisms. As such, functional connectivity provides a tool for understanding what brain regions may be communicating while engaging in specific cognitive or affective processes, and therefore what brain circuits support performance and ability in different domains of cognition, emotion and/or social processing.

A major breakthrough in the development and application of functional connectivity methods for humans came in 1995, when Biswal and colleagues reported that spontaneous activity from regions in the right and left motor cortices was highly correlated even while an individual was resting (Biswal, Yetkin, Haughton, & Hyde, 1995). Of interest, this correlated activity was observed in the spontaneous low-frequency fluctuations of the BOLD signal (0.01–0.10 Hz), a frequency band that has often been discarded as noise in task-based studies, although such correlations can be seen at other frequencies as well. This work spurred a major field of exploration of what is now referred to as resting state functional connectivity. Importantly, this resting brain state activity is thought to consume a major portion of the body’s energy (~20%), despite the brain only being 2% of the body’s total mass (Fox & Raichle, 2007).

Furthermore, changes in metabolism due to engagement in a specific task are typically less than 5%. Thus, ongoing resting state activity may provide a rich source of pathology-related variability over and above changes observed in the context of task performance (Fox & Raichle, 2007). In addition, there is also robust work demonstrating that a large portion of the trial-to-trial variability in task-related activity reflects coherent and organized spontaneous fluctuations in brain activity (Fox, Snyder, Zacks, & Raichle, 2006), providing another piece of evidence that this is a meaningful source of variation in human brain function.

One of the key aspects of resting state functional connectivity that has spurred interest in this aspect of brain function is the realization that it reveals intrinsically organized networks of brain regions that are consistently functionally connected, even in the absence of task-induced perturbations in ongoing brain activity (Fox et al., 2005). This has been supported by numerous “network” mapping studies that have identified consistent, robust and reproducible networks of brain regions that show coordinated activity at rest (Buckner, Krienen, Castellanos, Diaz, & Yeo, 2011; Choi, Yeo, & Buckner, 2012; Craddock, James, Holtzheimer, Hu, & Mayberg, 2012; Gordon et al., 2016; Laird et al., 2011; Power et al., 2011; Smith et al., 2012; Wig et al., 2014; Yeo et al., 2011). These include the default mode network, the frontal parietal network, the cingulo-opercular network, and the dorsal attention network, to name a few.

Importantly, many of these networks map closely or at least partially, to networks that have been identified in task-based studies, providing validation to the functional meaningfulness of this coordinated activity (Laird et al., 2011). Such functional connectivity networks have their basis in part in structural connectivity (Betzel et al., 2014; Hermundstad et al., 2013; Horn & Blankenburg, 2015; Horn, Ostwald, Reisert, & Blankenburg, 2014; Messe, Rudrauf, Giron, & Marrelec, 2015; Miranda-Dominguez et al., 2014), but are not isomorphic with regions that show direct structural connectivity. As such, the mapping of resting state networks to known task networks has led to the hypothesis that resting state networks reflect in part organized interactions that arise from a history of co-activation over development (Power et al., 2011). Such networks are now frequently referred to as “intrinsic” connectivity networks, given that they are not dependent on performance of a particular task for identification, are present even at rest and are relatively consistent across task and environmental states. Many of the articles in this special issue focus on these networks to understand the source of pathology in various forms of brain dysfunction.

A second goal of this special issue is to provide integrative and synthetic summaries of the state-of-the field in our current understanding of brain connectivity impairments associated with both neurological and neuropsychiatric disorders. As such, the article by Hayes and colleagues reviews the current state of the literature on understanding traumatic brain injury (TBI) as a disorder of brain connectivity. Much of the work on TBI has long focused on the impairments in white matter that may occur as a result of the different traumas that can lead to TBI, with the recognition that damage to white matter connections may be occurring even when focal lesions are not found. The article by Hayes et al. provides evidence that TBI is indeed associated with white matter damage. The corpus callosum seems to be particularly vulnerable to the forces that lead to TBI, but the existing evidence suggest that the damage associated with TBI can be quite diffuse and present in many different tracts, perhaps dependent on the nature and severity of the causal trauma. This white matter damage persists even into chronic phases of TBI and has been associated with cognitive and functional impairments. Furthermore, TBI is also associated with alterations in functional connectivity, with several studies highlighting disrupted connectivity of the default mode network and its contribution to post concussion symptoms and impairments in attention focus.

The review article by Teipel and colleagues highlights the potentially important roles of both structural and functional connectivity in the evolution of Alzheimer’s disease (AD). As described in this review article, there are now numerous cross-sectional and longitudinal studies relevant to AD documenting impairments in white matter, including abnormalities in limbic tracts such as the fornix, the uncinate fasciculus and the posterior and parahippocampal fibers of the cingulum. Furthermore, there is growing evidence of abnormalities in resting state connectivity, with several studies demonstrating abnormalities of the default mode network. Abnormalities in connectivity have also been documented using coherence measures in EEG. Importantly, there is increasing evidence of links between structural and functional connectivity impairments in the default mode network in AD. As noted by Teipel et al., these findings are helping us to understand the potential etiological mechanisms of AD relevant pathology, as well as the longitudinal evolution of the disease. However, more work is needed for such findings to have direct clinical application. On the opposite end of the life span, Koyama and colleagues also argue for the importance of connectivity in understanding the developmental trajectory of children who may be at risk for the development of a range of disorders that impact brain function.

The third goal of this special issue is to present cutting-edge empirical findings on the nature and role of connectivity impairments in understanding variations in cognitive and emotional function, as well as in a range of clinical populations. To this end, the special issue includes several novel empirical findings on ways in which either structural or functional connectivity contributes to cognitive and affective function in both health and disease. Several papers provide data on the relationships between behavior and brain connectivity in healthy individuals, providing evidence for the functional significance of individual variation in brain connectivity. For example, Unger and colleagues demonstrate that individual differences in the integrity of the inferior longitudinal fasciculus (ILF) and the inferior fronto-occipital fasciculus (IFOF) predict emotion recognition performance as well as memory for emotional faces. Ly and colleagues provide evidence for a relationship between the microstructure of the fornix and both task-related connectivity and performance during episodic recognition in healthy aging, providing an interesting model of potential pathways to individual differences in memory.

The other empirical papers in this special issue demonstrate how abnormalities in structural and or functional connectivity may contribute to behavioral and affective impairments in various forms of brain pathology. For example, Putcha and colleagues provide intriguing evidence that altered coupling of the salience and default mode networks relates to cognitive function in both healthy individuals and those with Parkinson’s disease. The salience and default mode networks are typically “anti-correlated,” and it is thought that the ability to down-regulate activity of the default mode network during cognitive tasks is important for effective performance. The finding by Putcha et al. that a lack of anti-correlation was associated with impaired function in executive, memory, and psychomotor speed domains is consistent with this hypothesis.

Relatedly, Dobryakova and colleagues provide evidence that altered connectivity among frontal-parietal regions predicts impaired processing speed in multiple sclerosis. Rao and colleagues examined connectivity of hippocampus to both fronto-temporal and fronto-parietal regions among individuals with remitted major depression compared to controls during semantically cued episodic memory performance. They found evidence for impaired memory performance and altered hippocampal connectivity among the previously depressed individuals, along with evidence for disrupted relationships between hippocampal connectivity and memory.

Finally, three articles bring to bear a graph theoretic approach to understanding how alterations in neural networks contribute to the development of brain pathology and associated impairments in cognitive function. Graph theory is a branch of mathematics that provides algorithms for determining metrics that characterize networks at both global and local levels of function. One major advantage of graph theory is its flexibility; algorithms can be applied to functional connectivity data, structural connectivity data, as well as to data obtained using MEG, EEG, or fMRI, allowing for a convergence of findings across differing modalities.

Furthermore, network science allows for the characterization of dynamic processes through single metrics, arguably providing more powerful and parsimonious descriptions of heterogeneous data than the previously discussed approaches. Although much work is needed to further validate the use of graph theory for interpreting brain connectivity data, network science represents an exciting new field of research that is increasingly showing associations between network metrics and behavior, as well as abnormalities in networks metrics in individuals with psychiatric and neurological disorders.

Yeo and colleagues use the power of a graph theory approach to show that the structural brain networks of individuals with schizophrenia are characterized by longer characteristic path lengths (suggesting a longer transit time for information) and reduced overall connectivity. These graph theory metrics predicted overall cognitive ability in healthy individuals as well as in individuals with schizophrenia, and the overall connectivity reduction mediated the relationship between diagnosis and overall cognitive ability.

Furthermore, Yeo et al. found that a genetic measure reflecting mutation load predicted both longer characteristic path length and global cognitive ability. Sedeno and colleagues also took a graph theory approach to understanding the mechanisms that might contribute to cognitive and social impairments in fronto-temporal dementia. Used resting state functional connectivity, they showed decreased network centrality, a measure of the importance of a node or brain region’s role in a network, in the fronto-temporal–insular network. Furthermore, this network centrality was associated with social cognition and executive function in individuals with fronto-temporal dementia and healthy individuals. Finally, Han and colleagues examined networks associated with goal-directed behavior in individuals with chronic TBI. They observed markedly disrupted long-range interhemispheric and between-network connectivity between the default mode network, the dorsal attention network, and the frontoparietal control network, with as result reduced global and local efficiency.

In summary, we believe that the articles presented in this special issue provide an important entryway into the burgeoning literature on the role of neural networks in cognition and the nature of alterations in circuit level structural and functional connectivity associated with brain pathology. As noted in many of the articles, this field continues to evolve as both the acquisition and analyses methods develop and expand, but the existing evidence points to the functional relevance of these networks to understanding variations in normal development and the critical importance of their structural and functional brain disruption as a means of understanding pathophysiology and potential pathways for intervention or even prevention.


Individual Titles, Authors, and Articles:

Modern Methods for Interrogating the Human Connectome
Author(s)
  • Mark J. Lowe | Imaging Institute, Cleveland Clinic, Cleveland, Ohio
  • Ken E. Sakaie | Imaging Institute, Cleveland Clinic, Cleveland, Ohio
  • Erik B. Beall | Imaging Institute, Cleveland Clinic, Cleveland, Ohio
  • Vince D. Calhoun | The Mind Research Network, Albuquerque, New Mexico, Department of ECE, University of New Mexico, Albuquerque, New Mexico
  • David A. Bridwell | The Mind Research Network, Albuquerque, New Mexico, Department of ECE, University of New Mexico, Albuquerque, New Mexico
  • Mikail Rubinov | Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
  • Stephen M. Rao | Neurological Institute, Cleveland Clinic, Cleveland, Ohio

Correspondence
E-mail address | raos2@ccf.org

Disclosures
The authors do not report conflicts of interest related to this manuscript.

Abstract
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)

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Traumatic Brain Injury as a Disorder of Brain Connectivity
Author(s)
  • Jasmeet P. Hayes | National Center for PTSD, VA Boston Healthcare System, Boston, Massachusetts, Department of Psychiatry, Boston University School of Medicine, Boston, Massachusetts, Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, Massachusetts
  • Erin D. Bigler | Department of Psychology, Brigham Young University, Provo, Utah, Neuroscience Center, Brigham Young University, Provo, Utah, Department of Psychiatry, University of Utah, Salt Lake City, Utah
  • Mieke Verfaellie | Department of Psychiatry, Boston University School of Medicine, Boston, Massachusetts, Memory Disorders Research Center, VA Boston Healthcare System, Boston, Massachusetts

Correspondence
E-mail address | jphayes@bu.edu

Disclosures
here are no financial disclosures or conflicts of interest to report for any of the authors.

Abstract
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)

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Measuring Cortical Connectivity in Alzheimer’s Disease as a Brain Neural Network Pathology: Toward Clinical Applications
Author(s)
  • Stefan Teipel | Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany, DZNE, German Center for Neurodegenerative Diseases, Rostock, Germany
  • Michel J. Grothe | DZNE, German Center for Neurodegenerative Diseases, Rostock, Germany
  • Juan Zhou | Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-NUS Graduate Medical School, Singapore
  • Jorge Sepulcre | Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
  • Martin Dyrba | DZNE, German Center for Neurodegenerative Diseases, Rostock, Germany
  • Christian Sorg | Department of Psychiatry and Neuroradiology, TUM-NIC Neuroimaging Center, Technische Universität München, Munich, Germany
  • Claudio Babiloni | Department of Physiology and Pharmacology “V. Erspamer”, University of Rome “La Sapienza”, Rome, Italy; IRCCS San Raffaele Pisana of Rome, Italy

Correspondence

Disclosures
There are no conflicts of interest related to this manuscript.

Abstract
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)

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Imaging the "At-Risk" Brain: Future Directions
Author(s)
  • Maki S. Koyama | Child Mind Institute, New York, New York, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York
  • Adriana Di Martino | The Child Study Center at NYU Langone Medical Center, New York, New York
  • Francisco X. Castellanos | Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York, The Child Study Center at NYU Langone Medical Center, New York, New York
  • Erica J. Ho | Child Mind Institute, New York, New York
  • Enitan Marcelle | Child Mind Institute, New York, New York
  • Bennett Leventhal | Department of Psychiatry University of California–San Francisco, San Francisco, California
  • Michael P. Milham | Child Mind Institute, New York, New York, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York

Correspondence

Disclosures
The authors declare that there are no conflicts of interest.

Abstract
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)

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Variation in White Matter Connectivity Predicts the Ability to Remember Faces and Discriminate Their Emotions
Author(s)
  • Ashley Unger | Temple University, Department of Psychology, Philadelphia, Pennsylvania
  • Kylie H. Alm | Temple University, Department of Psychology, Philadelphia, Pennsylvania
  • Jessica A. Collins | Massachusetts General Hospital, Department of Neurology, Boston, Massachusetts
  • Jacquelyn M. O’Leary | Temple University, Department of Psychology, Philadelphia, Pennsylvania
  • Ingrid R. Olson | Temple University, Department of Psychology, Philadelphia, Pennsylvania

Correspondence
E-mail address | iolson@temple.edu

Disclosures
The authors declare no competing or conflicting financial interests.

Abstract
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, theuncinate 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)

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Fornix Microstructure and Memory Performance Is Associated with Altered Neural Connectivity during Episodic Recognition
Author(s)
  • Martina Ly | Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veteran’s Hospital, Madison, Wisconsin, Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, Neuroscience Training Program, University of Wisconsin, Madison, Wisconsin, Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin, Madison, Wisconsin
  • Nagesh Adluru | Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin, Madison, Wisconsin
  • Daniel J. Destiche | Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin, Madison, Wisconsin
  • Sharon Y. Lu | Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin, Madison, Wisconsin
  • Jennifer M. Oh | Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veteran’s Hospital, Madison, Wisconsin, Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
  • Siobhan M. Hoscheidt | Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veteran’s Hospital, Madison, Wisconsin, Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
  • Andrew L. Alexander | Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin, Madison, Wisconsin, Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
  • Ozioma C. Okonkwo | Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veteran’s Hospital, Madison, Wisconsin, Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
  • Howard A. Rowley | Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
  • Mark A. Sager | Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veteran’s Hospital, Madison, Wisconsin, Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
  • Sterling C. Johnson | Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veteran’s Hospital, Madison, Wisconsin, Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
  • Barbara B. Bendlin | Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veteran’s Hospital, Madison, Wisconsin, Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin

Correspondence
E-mail address | bbb@medicine.wisc.edu

Disclosures
Ly, Adluru, Destiche, Lu, Oh, Hoscheidt, Okonkwo, Sager, Johnson, and Bendlin report no conflicts of interest. Rowley has provided consultation to and/or received honoraria from GE Healthcare, Bracco, Lundbeck, HL Gore, and Eli Lilly. Alexander is part owner and Chief Operating Officer of inSERT, Inc.

Abstract
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)

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Salience and Default Mode Network Coupling Predicts Cognition in Aging and Parkinson’s Disease
Author(s)
  • Deepti Putcha | Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts
  • Robert S. Ross | Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, Department of Psychology, University of New Hampshire, Durham, New Hampshire
  • Alice Cronin-Golomb | Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts
  • Amy C. Janes | Brain Imaging Center, McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, Massachusetts
  • Chantal E. Stern | Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts

Correspondence
E-mail address | dputcha@bu.edu

Disclosures
The authors declare no competing financial interests.

Abstract
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)

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Altered Effective Connectivity during a Processing Speed Task in Individuals with Multiple Sclerosis
Author(s)
  • E. Dobryakova | Kessler Foundation, Pleasant Valley Way, West Orange, New Jersey, Rutgers, New Jersey Medical School, Newark, New Jersey
  • S.L. Costa | Rutgers, New Jersey Medical School, Newark, New Jersey, Kessler Foundation, Executive Drive, West Orange, New Jersey
  • G.R. Wylie | Rutgers, New Jersey Medical School, Newark, New Jersey, Kessler Foundation, Executive Drive, West Orange, New Jersey, War Related Illness & Injury Study Center, Department of Veteran’s Affairs, East Orange, New Jersey
  • J. DeLuca | Kessler Foundation, Pleasant Valley Way, West Orange, New Jersey, Rutgers, New Jersey Medical School, Newark, New Jersey
  • H.M. Genova | Rutgers, New Jersey Medical School, Newark, New Jersey, Kessler Foundation, Executive Drive, West Orange, New Jersey

Correspondence

Disclosures
The authors declare no conflict of interest.

Abstract
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 impairedvs. 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)

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Differential Resting State Connectivity Patterns and Impaired Semantically Cued List Learning Test Performance in Early Course Remitted Major Depressive Disorder
Author(s)
  • Julia A. Rao | University of Illinois at Chicago, Department of Psychiatry, Chicago, Illinois
  • Lisanne M. Jenkins | University of Illinois at Chicago, Department of Psychiatry, Chicago, Illinois
  • Erica Hymen | University of Illinois at Chicago, Department of Psychiatry, Chicago, Illinois
  • Maia Feigon | University of Illinois at Chicago, Department of Psychiatry, Chicago, Illinois
  • Sara L. Weisenbach | University of Illinois at Chicago, Department of Psychiatry, Chicago, Illinois, University of Michigan Medical Center, Department of Psychiatry, Ann Arbor, Michigan, Jesse Brown Veterans Administration Hospital, Research & Development Program, Chicago, Illinois
  • Jon-Kar Zubieta | University of Michigan Medical Center, Department of Psychiatry, Ann Arbor, Michigan
  • Scott A. Langenecker | University of Illinois at Chicago, Department of Psychiatry, Chicago, Illinois, University of Michigan Medical Center, Department of Psychiatry, Ann Arbor, Michigan

Correspondence

Disclosures
There are no competing interests to declare for all authors.

Abstract
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)

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Graph Metrics of Structural Brain Networks in Individuals with Schizophrenia and Healthy Controls: Group Differences, Relationships with Intelligence, and Genetics
Author(s)
  • Ronald A. Yeo | Department of Psychology, University of New Mexico, Albuquerque, New Mexico
  • Sephira G. Ryman | Department of Psychology, University of New Mexico, Albuquerque, New Mexico, The Mind Research Network, Albuquerque, New Mexico
  • Martijn P. van den Heuvel | Department of Psychiatry, Brain Center Rudolph Magnus, University Medical Center Utrecht, Netherlands
  • Marcel A. de Reus | Department of Psychiatry, Brain Center Rudolph Magnus, University Medical Center Utrecht, Netherlands
  • Rex E. Jung | Department of Psychology, University of New Mexico, Albuquerque, New Mexico, Department of Neurosurgery, University of New Mexico, Albuquerque, New Mexico
  • Jessica Pommy | Department of Psychology, University of New Mexico, Albuquerque, New Mexico
  • Andrew R. Mayer | Department of Psychology, University of New Mexico, Albuquerque, New Mexico, The Mind Research Network, Albuquerque, New Mexico
  • Stefan Ehrlich | MGH/MIT/HMS Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, Department of Child and Adolescent Psychiatry, University Hospital Carl Gustav Carus, Dresden University of Technology, Dresden, Germany, Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts
  • S. Charles Schulz | Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
  • Eric M. Morrow | Department of Molecular Biology, Cell Biology and Biochemistry, Laboratory for Molecular Medicine, Brown University, Providence, Rhode Island
  • Dara Manoach | Psychiatric Neuroimaging and Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital, Charlestown, Massachusetts
  • Beng-Choon Ho | Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, Iowa
  • Scott R. Sponheim | Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota, Minneapolis Veterans Administration Health Care System, Minneapolis, New Mexico
  • Vince D. Calhoun | The Mind Research Network, Albuquerque, New Mexico, Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico

Correspondence
E-mail address | ryeo@unm.edu

Disclosures
The authors report no conflicts of interest.

Abstract
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)