Fenna M. Krienen

The organization of the human cerebral cortex estimated by intrinsic functional connectivity

Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.

Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease

Recent evidence suggests that some brain areas act as hubs interconnecting distinct, functionally specialized systems. These nexuses are intriguing because of their potential role in integration and also because they may augment metabolic cascades relevant to brain disease. To identify regions of high connectivity in the human cerebral cortex, we applied a computationally efficient approach to map the degree of intrinsic functional connectivity across the brain. Analysis of two separate functional magnetic resonance imaging datasets (each n = 24) demonstrated hubs throughout heteromodal areas of association cortex. Prominent hubs were located within posterior cingulate, lateral temporal, lateral parietal, and medial/lateral prefrontal cortices. Network analysis revealed that many, but not all, hubs were located within regions previously implicated as components of the default network. A third dataset (n = 12) demonstrated that the locations of hubs were present across passive and active task states, suggesting that they reflect a stable property of cortical network architecture. To obtain an accurate reference map, data were combined across 127 participants to yield a consensus estimate of cortical hubs. Using this consensus estimate, we explored whether the topography of hubs could explain the pattern of vulnerability in Alzheimers disease (AD) because some models suggest that regions of high activity and metabolism accelerate pathology. Positron emission tomography amyloid imaging in AD (n = 10) compared with older controls (n = 29) showed high amyloid-β deposition in the locations of cortical hubs consistent with the possibility that hubs, while acting as critical way stations for information processing, may also augment the underlying pathological cascade in AD.

Segregated Fronto-Cerebellar Circuits Revealed by Intrinsic Functional Connectivity

Multiple, segregated fronto-cerebellar circuits have been characterized in nonhuman primates using transneuronal tracing techniques including those that target prefrontal areas. Here, we used functional connectivity MRI (fcMRI) in humans (n = 40) to identify 4 topographically distinct fronto-cerebellar circuits that target 1) motor cortex, 2) dorsolateral prefrontal cortex, 3) medial prefrontal cortex, and 4) anterior prefrontal cortex. All 4 circuits were replicated and dissociated in an independent data set (n = 40). Direct comparison of right- and left-seeded frontal regions revealed contralateral lateralization in the cerebellum for each of the segregated circuits. The presence of circuits that involve prefrontal regions confirms that the cerebellum participates in networks important to cognition including a specific fronto-cerebellar circuit that interacts with the default network. Overall, the extent of the cerebellum associated with prefrontal cortex included a large portion of the posterior hemispheres consistent with a prominent role of the cerebellum in nonmotor functions. We conclude by providing a provisional map of the topography of the cerebellum based on functional correlations with the frontal cortex.

Opportunities and limitations of intrinsic functional connectivity MRI

Intrinsic functional connectivity magnetic resonance imaging (fcMRI) has emerged as a powerful tool for mapping large-scale networks in the human brain. Robust and reliable functionally coupled networks can be detected in individuals that echo many known features of anatomical organization. Features of brain organization have been discovered, including descriptions of distributed large-scale networks interwoven throughout association cortex, interactions (including anticorrelations) between brain networks and insights into the topography of subcortical structures. But interpreting fcMRI is complicated by several factors. Functional coupling changes dynamically, suggesting that it is constrained by, but not fully dictated by, anatomic connectivity. Critically to study of between-group differences, fcMRI is sensitive to head motion and to differences in the mental states of participants during the scans. We discuss the potential of fcMRI in the context of its limitations.

The evolution of distributed association networks in the human brain

The human cerebral cortex is vastly expanded relative to other primates and disproportionately occupied by distributed association regions. Here we offer a hypothesis about how association networks evolved their prominence and came to possess circuit properties vital to human cognition. The rapid expansion of the cortical mantle may have untethered large portions of the cortex from strong constraints of molecular gradients and early activity cascades that lead to sensory hierarchies. What fill the gaps between these hierarchies are densely interconnected networks that widely span the cortex and mature late into development. Limitations of the tethering hypothesis are discussed as well as its broad implications for understanding critical features of the human brain as a byproduct of size scaling.

Disruption of Cortical Association Networks in Schizophrenia and Psychotic Bipolar Disorder

IMPORTANCE Psychotic disorders (including schizophrenia, schizoaffective disorder, and psychotic bipolar disorder) are devastating illnesses characterized by breakdown in the integration of information processing. Recent advances in neuroimaging allow for the estimation of brain networks on the basis of intrinsic functional connectivity, but the specific network abnormalities in psychotic disorders are poorly understood. OBJECTIVE To compare intrinsic functional connectivity across the cerebral cortex in patients with schizophrenia spectrum disorders or psychotic bipolar disorder and healthy controls. DESIGN, SETTING, AND PARTICIPANTS We studied 100 patients from an academic psychiatric hospital (28 patients with schizophrenia, 32 patients with schizoaffective disorder, and 40 patients with bipolar disorder with psychosis) and 100 healthy controls matched for age, sex, race, handedness, and scan quality from December 2009 to October 2011. MAIN OUTCOMES AND MEASURES Functional connectivity profiles across 122 regions that covered the entire cerebral cortex. RESULTS Relative to the healthy controls, individuals with a psychotic illness had disruption across several brain networks, with preferential reductions in functional connectivity within the frontoparietal control network (P < .05, corrected for family-wise error rate). This functionally defined network includes portions of the dorsolateral prefrontal cortex, posteromedial prefrontal cortex, lateral parietal cortex, and posterior temporal cortex. This effect was seen across diagnoses and persisted after matching patients and controls on the basis of scan quality. CONCLUSIONS AND RELEVANCE Our study results support the view that cortical information processing is disrupted in psychosis and provides new evidence that disruptions within the frontoparietal control network may be a shared feature across both schizophrenia and affective psychosis.

Functional Specialization and Flexibility in Human Association Cortex

The association cortex supports cognitive functions enabling flexible behavior. Here, we explored the organization of human association cortex by mathematically formalizing the notion that a behavioral task engages multiple cognitive components, which are in turn supported by multiple overlapping brain regions. Application of the model to a large data set of neuroimaging experiments (N = 10 449) identified complex zones of frontal and parietal regions that ranged from being highly specialized to highly flexible. The network organization of the specialized and flexible regions was explored with an independent resting-state fMRI data set (N = 1000). Cortical regions specialized for the same components were strongly coupled, suggesting that components function as partially isolated networks. Functionally flexible regions participated in multiple components to different degrees. This heterogeneous selectivity was predicted by the connectivity between flexible and specialized regions. Functionally flexible regions might support binding or integrating specialized brain networks that, in turn, contribute to the ability to execute multiple and varied tasks.

Reconfigurable task-dependent functional coupling modes cluster around a core functional architecture

Functional coupling across distributed brain regions varies across task contexts, yet there are stable features. To better understand the range and central tendencies of network configurations, coupling patterns were explored using functional MRI (fMRI) across 14 distinct continuously performed task states ranging from passive fixation to increasingly demanding classification tasks. Mean global correlation profiles across the cortex ranged from 0.69 to 0.82 between task states. Network configurations from both passive fixation and classification tasks similarly predicted task coactivation patterns estimated from meta-analysis of the literature. Thus, even across markedly different task states, central tendencies dominate the coupling configurations. Beyond these shared components, distinct task states displayed significant differences in coupling patterns in response to their varied demands. One possibility is that anatomical connectivity provides constraints that act as attractors pulling network configurations towards a limited number of robust states. Reconfigurable coupling modes emerge as significant modifications to a core functional architecture.

Estimates of Segregation and Overlap of Functional Connectivity Networks in the Human Cerebral Cortex

The organization of the human cerebral cortex has recently been explored using techniques for parcellating the cortex into distinct functionally coupled networks. The divergent and convergent nature of cortico-cortical anatomic connections suggests the need to consider the possibility of regions belonging to multiple networks and hierarchies among networks. Here we applied the Latent Dirichlet Allocation (LDA) model and spatial independent component analysis (ICA) to solve for functionally coupled cerebral networks without assuming that cortical regions belong to a single network. Data analyzed included 1000 subjects from the Brain Genomics Superstruct Project (GSP) and 12 high quality individual subjects from the Human Connectome Project (HCP). The organization of the cerebral cortex was similar regardless of whether a winner-take-all approach or the more relaxed constraints of LDA (or ICA) were imposed. This suggests that large-scale networks may function as partially isolated modules. Several notable interactions among networks were uncovered by the LDA analysis. Many association regions belong to at least two networks, while somatomotor and early visual cortices are especially isolated. As examples of interaction, the precuneus, lateral temporal cortex, medial prefrontal cortex and posterior parietal cortex participate in multiple paralimbic networks that together comprise subsystems of the default network. In addition, regions at or near the frontal eye field and human lateral intraparietal area homologue participate in multiple hierarchically organized networks. These observations were replicated in both datasets and could be detected (and replicated) in individual subjects from the HCP.

Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain

Significance The human cerebral cortex is patterned with distributed networks that connect disproportionately enlarged association zones across the frontal, temporal, and parietal lobes. We asked herein whether the expansion of the cortical surface, with the concomitant emergence of long-range connectivity networks, might be accompanied by changes to the underlying molecular architecture. We focused on the supragranular layers of the cortex, where most corticocortical connections originate. Genes that are enriched in supragranular layers in the human cerebral cortex relative to mouse are expressed in a topography that reflects broad cortical classes (sensory/motor, paralimbic, associational) and their associated network properties. Molecular innovations of upper cortical layers may be an important component in the evolution of increased long-range corticocortical projections. The human brain is patterned with disproportionately large, distributed cerebral networks that connect multiple association zones in the frontal, temporal, and parietal lobes. The expansion of the cortical surface, along with the emergence of long-range connectivity networks, may be reflected in changes to the underlying molecular architecture. Using the Allen Institute’s human brain transcriptional atlas, we demonstrate that genes particularly enriched in supragranular layers of the human cerebral cortex relative to mouse distinguish major cortical classes. The topography of transcriptional expression reflects large-scale brain network organization consistent with estimates from functional connectivity MRI and anatomical tracing in nonhuman primates. Microarray expression data for genes preferentially expressed in human upper layers (II/III), but enriched only in lower layers (V/VI) of mouse, were cross-correlated to identify molecular profiles across the cerebral cortex of postmortem human brains (n = 6). Unimodal sensory and motor zones have similar molecular profiles, despite being distributed across the cortical mantle. Sensory/motor profiles were anticorrelated with paralimbic and certain distributed association network profiles. Tests of alternative gene sets did not consistently distinguish sensory and motor regions from paralimbic and association regions: (i) genes enriched in supragranular layers in both humans and mice, (ii) genes cortically enriched in humans relative to nonhuman primates, (iii) genes related to connectivity in rodents, (iv) genes associated with human and mouse connectivity, and (v) 1,454 gene sets curated from known gene ontologies. Molecular innovations of upper cortical layers may be an important component in the evolution of long-range corticocortical projections.