Hippocampal cingulum white matter increases over time in young people at high genetic risk for bipolar disorder.

BACKGROUND: Bipolar disorder (BD) is a strongly familial psychiatric disorder associated with white matter (WM) brain abnormalities. It is unclear whether such abnormalities are present in relatives without BD, and little is known about WM trajectories in those at increased genetic risk. METHODS: Diffusion magnetic resonance imaging (dMRI) data were acquired at baseline and after two years in 91 unaffected individuals with a first-degree relative with bipolar disorder (HR), and 85 individuals with no family history of mental illness (CON). All participants were aged between 12 and 30 years at baseline. We examined longitudinal change in Fractional Anisotropy (FA) using tract-based spatial statistics (TBSS). RESULTS: Compared to the CON group, HR participants showed a significant increase in FA in the right cingulum (hippocampus) (CGH) over a two-year period (p < .05, FDR corrected). This effect was more pronounced in HR individuals without a lifetime diagnosis of a mood disorder than those with a mood disorder. LIMITATIONS: While our study is well powered to achieve the primary objectives, our sub-group analyses were under powered. CONCLUSIONS: In one of the very few longitudinal neuroimaging studies of young people at high risk for BD, this study reports novel evidence of atypical white matter development in HR individuals in a key cortico-limbic tract involved in emotion regulation. Our findings also suggest that this different white matter developmental trajectory may be stronger in HR individuals without affective psychopathology. As such, increases in FA in the right CGH of HR participants may be a biomarker of resilience to mood disorders.

Accelerated cortical thinning and volume reduction over time in young people at high genetic risk for bipolar disorder.

BACKGROUND: Bipolar disorder (BD) is a familial psychiatric disorder associated with frontotemporal and subcortical brain abnormalities. It is unclear whether such abnormalities are present in relatives without BD, and little is known about structural brain trajectories in those at risk. METHOD: Neuroimaging was conducted at baseline and at 2-year follow-up interval in 90 high-risk individuals with a first-degree BD relative (HR), and 56 participants with no family history of mental illness who could have non-BD diagnoses. All 146 subjects were aged 12-30 years at baseline. We examined longitudinal change in gray and white matter volume, cortical thickness, and surface area in the frontotemporal cortex and subcortical regions. RESULTS: Compared to controls, HR participants showed accelerated cortical thinning and volume reduction in right lateralised frontal regions, including the inferior frontal gyrus, lateral orbitofrontal cortex, frontal pole and rostral middle frontal gyrus. Independent of time, the HR group had greater cortical thickness in the left caudal anterior cingulate cortex, larger volume in the right medial orbitofrontal cortex and greater area of right accumbens, compared to controls. This pattern was evident even in those without the new onset of psychopathology during the inter-scan interval. CONCLUSIONS: This study suggests that differences previously observed in BD are developing prior to the onset of the disorder. The pattern of pathological acceleration of cortical thinning is likely consistent with a disturbance of molecular mechanisms responsible for normal cortical thinning. We also demonstrate that neuroanatomical differences in HR individuals may be progressive in some regions and stable in others.

Differentiation of Alzheimer's disease based on local and global parameters in personalized Virtual Brain models.

Alzheimer's disease (AD) is marked by cognitive dysfunction emerging from neuropathological processes impacting brain function. AD affects brain dynamics at the local level, such as changes in the balance of inhibitory and excitatory neuronal populations, as well as long-range changes to the global network. Individual differences in these changes as they relate to behaviour are poorly understood. Here, we use a multi-scale neurophysiological model, "The Virtual Brain (TVB)", based on empirical multi-modal neuroimaging data, to study how local and global dynamics correlate with individual differences in cognition. In particular, we modeled individual resting-state functional activity of 124 individuals across the behavioural spectrum from healthy aging, to amnesic Mild Cognitive Impairment (MCI), to AD. The model parameters required to accurately simulate empirical functional brain imaging data correlated significantly with cognition, and exceeded the predictive capacity of empirical connectomes.

Structural dysconnectivity of key cognitive and emotional hubs in young people at high genetic risk for bipolar disorder.

Emerging evidence suggests that psychiatric disorders are associated with disturbances in structural brain networks. Little is known, however, about brain networks in those at high risk (HR) of bipolar disorder (BD), with such disturbances carrying substantial predictive and etiological value. Whole-brain tractography was performed on diffusion-weighted images acquired from 84 unaffected HR individuals with at least one first-degree relative with BD, 38 young patients with BD and 96 matched controls (CNs) with no family history of mental illness. We studied structural connectivity differences between these groups, with a focus on highly connected hubs and networks involving emotional centres. HR participants showed lower structural connectivity in two lateralised sub-networks centred on bilateral inferior frontal gyri and left insular cortex, as well as increased connectivity in a right lateralised limbic sub-network compared with CN subjects. BD was associated with weaker connectivity in a small right-sided sub-network involving connections between fronto-temporal and temporal areas. Although these sub-networks preferentially involved structural hubs, the integrity of the highly connected structural backbone was preserved in both groups. Weaker structural brain networks involving key emotional centres occur in young people at genetic risk of BD and those with established BD. In contrast to other psychiatric disorders such as schizophrenia, the structural core of the brain remains intact, despite the local involvement of network hubs. These results add to our understanding of the neurobiological correlates of BD and provide predictions for outcomes in young people at high genetic risk for BD.

Are there subtypes of bipolar depression?

OBJECTIVE: To investigate for subtypes of bipolar depression using latent class analysis (LCA). METHOD: Participants were recruited through a bipolar disorder (BD) clinic. LCA was undertaken using: (i) symptoms reported on the SCID-IV for the most severe lifetime depressive episode; (ii) lifetime illness features such as age at first depressive and hypo/manic episodes; and (iii) family history of BD and unipolar depression. To explore the validity of any demonstrated 'classes', clinical, demographic and treatment correlates were investigated. RESULTS: A total of 243 BD subjects (170 with BD-I and 73 with BD-II) were included. For the combined sample, we found two robust LCA solutions, with two and three classes respectively. There were no consistent solutions when the BD-I and BD-II samples were considered separately. Subjects in class 2 of the three-class solution (characterised by anxiety, insomnia, reduced appetite/weight loss, irritability, psychomotor retardation, suicidal ideation, guilt, worthlessness and evening worsening) were significantly more likely to be in receipt of government financial support, suggesting a particularly malign pattern of symptoms. CONCLUSION: Our study suggests the existence of two or three distinct classes of bipolar depression and a strong association with functional outcome.

Interhemispheric white matter integrity in young people with bipolar disorder and at high genetic risk.

BACKGROUND: White matter (WM) impairments have been reported in patients with bipolar disorder (BD) and those at high familial risk of developing BD. However, the distribution of these impairments has not been well characterized. Few studies have examined WM integrity in young people early in the course of illness and in individuals at familial risk who have not yet passed the peak age of onset. METHOD: WM integrity was examined in 63 BD subjects, 150 high-risk (HR) individuals and 111 participants with no family history of mental illness (CON). All subjects were aged 12 to 30 years. RESULTS: This young BD group had significantly lower fractional anisotropy within the genu of the corpus callosum (CC) compared with the CON and HR groups. Moreover, the abnormality in the genu of the CC was also present in HR participants with recurrent major depressive disorder (MDD) (n = 16) compared with CON participants. CONCLUSIONS: Our findings provide important validation of interhemispheric abnormalities in BD patients. The novel finding in HR subjects with recurrent MDD - a group at particular risk of future hypo/manic episodes - suggests that this may potentially represent a trait marker for BD, though this will need to be confirmed in longitudinal follow-up studies.

Distinct neurobiological signatures of brain connectivity in depression subtypes during natural viewing of emotionally salient films.

BACKGROUND: Establishing an evidence-based diagnostic system informed by the biological (dys)function of the nervous system is a major priority in psychiatry. This objective, however, is often challenged by difficulties in identifying homogeneous clinical populations. Melancholia, a biological and endogenous subtype for major depressive disorder, presents a canonical test case in the search of biological nosology. METHOD: We employed a unique combination of naturalistic functional magnetic resonance imaging (fMRI) paradigms - resting state and free viewing of emotionally salient films - to search for neurobiological signatures of depression subtypes. fMRI data were acquired from 57 participants; 17 patients with melancholia, 17 patients with (non-melancholic) major depression and 23 matched healthy controls. RESULTS: Patients with melancholia showed a prominent loss of functional connectivity in hub regions [including ventral medial prefrontal cortex, anterior cingulate cortex (ACC) and superior temporal gyrus] during natural viewing, and in the posterior cingulate cortex while at rest. Of note, the default mode network showed diminished reactivity to external stimuli in melancholia, which correlated with the severity of anhedonia. Intriguingly, the subgenual ACC, a potential target for treating depression with deep brain stimulation (DBS), showed divergent changes between the two depression subtypes, with increased connectivity in the non-melancholic and decreased connectivity in the melancholic subsets. CONCLUSION: These findings reveal neurobiological changes specific to depression subtypes during ecologically valid behavioural conditions, underscoring the critical need to respect differing neurobiological processes underpinning depressive subtypes.

Deconvolution of neural dynamics from fMRI data using a spatiotemporal hemodynamic response function.

Functional magnetic resonance imaging (fMRI) is a powerful and broadly used means of non-invasively mapping human brain activity. However fMRI is an indirect measure that rests upon a mapping from neuronal activity to the blood oxygen level dependent (BOLD) signal via hemodynamic effects. The quality of estimated neuronal activity hinges on the validity of the hemodynamic model employed. Recent work has demonstrated that the hemodynamic response has non-separable spatiotemporal dynamics, a key property that is not implemented in existing fMRI analysis frameworks. Here both simulated and empirical data are used to demonstrate that using a physiologically based model of the spatiotemporal hemodynamic response function (stHRF) results in a quantitative improvement of the estimated neuronal response relative to unphysical space-time separable forms. To achieve this, an integrated spatial and temporal deconvolution is established using a recently developed stHRF. Simulated data allows the variation of key parameters such as noise and the spatial complexity of the neuronal drive, while knowing the neuronal input. The results demonstrate that the use of a spatiotemporally integrated HRF can avoid "ghost" neuronal responses that can otherwise be falsely inferred. Applying the spatiotemporal deconvolution to high resolution fMRI data allows the recovery of neuronal responses that are consistent with independent electrophysiological measures.

Evaluating the first 1000 patients referred to a specialist depression clinic: a case for tertiary referral facilities.

BACKGROUND: We report on the assessment and outcome of the first 1000 patients referred to a tertiary referral depression clinic established to assess the utility of diagnostic sub-typing on clinical course of illness. METHODS: Diagnostic, treatment recommendations, prognostic judgments and 12-week outcome data were examined. RESULTS: Nearly 40% of those with a primary mood disorder were diagnosed with bipolar disorder, of whom three-quarters received such a diagnosis for the first time. Alternative diagnoses or formulations were provided for 68% of the total sample, with the therapeutic paradigm altered for the majority (86%) of patients. Improvement rates were indicative of a higher level of improvement in those diagnosed with bipolar disorder (some 70%) compared to those with unipolar disorders (some 60%). Overall, however, rates of 'full remission' were low, being 2% and up to 12% for bipolar and unipolar patients respectively and perhaps reflecting the tertiary nature of the assessing clinical facility. Baseline clinician predictions were in the order of 60% accuracy in predicting outcome, irrespective of diagnostic grouping. LIMITATIONS: Anticipation factors (e.g. attending a specialist tertiary referral service) may have contributed non-specifically to outcome. Use of clinician-derived diagnoses rather than strict DSM-IV criteria limits comparisons to other studies. CONCLUSIONS: The high rates of a first-time bipolar diagnosis suggest that detection and diagnosis of this condition continues to be problematic. Low remission rates underline the chronic nature of many mood disorders, and the need for ongoing management given the high risk of relapse. Our findings offer support for the importance of identifying bipolar disorder and distinguishing depressive sub-types in order to shape more targeted treatments, a task that might be advanced by the establishment of more tertiary referral services.

Towards the virtual brain: network modeling of the intact and the damaged brain.

Neurocomputational models of large-scale brain dynamics utilizing realistic connectivity matrices have advanced our understanding of the operational network principles in the brain. In particular, spontaneous or resting state activity has been studied on various scales of spatial and temporal organization including those that relate to physiological, encephalographic and hemodynamic data. In this article we focus on the brain from the perspective of a dynamic network and discuss the role of its network constituents in shaping brain dynamics. These constituents include the brain's structural connectivity, the population dynamics of its network nodes and the time delays involved in signal transmission. In addition, no discussion of brain dynamics would be complete without considering noise and stochastic effects. In fact, there is mounting evidence that the interaction between noise and dynamics plays an important functional role in shaping key brain processes. In particular, we discuss a unifying theoretical framework that explains how structured spatio-temporal resting state patterns emerge from noise driven explorations of unstable or stable oscillatory states. Embracing this perspective, we explore the consequences of network manipulations to understand some of the brain's dysfunctions, as well as network effects that offer new insights into routes towards therapy, recovery and brain repair. These collective insights will be at the core of a new computational environment, the Virtual Brain, which will allow flexible incorporation of empirical data constraining the brain models to integrate, unify and predict network responses to incipient pathological processes.

Functional alterations in brain activation and deactivation in mild cognitive impairment in response to a graded working memory challenge.

AIM: To investigate dynamic changes in functional brain activity in mild cognitive impairment (MCI) in response to a graded working memory (WM) challenge with increasing memory load. METHODS: In an event-related functional magnetic resonance imaging (fMRI) study, 35 MCI and 22 cognitively normal subjects performed a visuospatial associative WM task with 3 load levels. Potential performance differences were controlled for by individually calibrating the number of items presented at each load. RESULTS: An interaction between group and WM load was observed during stimulus encoding. At lower loads, greater activity in the right anterior cingulate and right precuneus was observed in MCI subjects. As the load increased to higher levels, reduced activation in these regions and greater deactivation in the posterior cingulate-medial precuneus were observed in MCI compared to control subjects. Stronger expression of load-related patterns of activation and deactivation in MCI subjects was associated with greater clinical severity and a more abnormal pattern of performance variability. CONCLUSION: Patterns of overactivation, underactivation and deactivation during successful encoding in MCI subjects were dependent on WM load. This type of graded cognitive challenge may operate like a 'memory stress test' in MCI and may be a useful biomarker of disease at the pre-dementia stage.

Small-world networks and functional connectivity in Alzheimer's disease.

We investigated whether functional brain networks are abnormally organized in Alzheimer's disease (AD). To this end, graph theoretical analysis was applied to matrices of functional connectivity of beta band-filtered electroencephalography (EEG) channels, in 15 Alzheimer patients and 13 control subjects. Correlations between all pairwise combinations of EEG channels were determined with the synchronization likelihood. The resulting synchronization matrices were converted to graphs by applying a threshold, and cluster coefficients and path lengths were computed as a function of threshold or as a function of degree K. For a wide range of thresholds, the characteristic path length L was significantly longer in the Alzheimer patients, whereas the cluster coefficient C showed no significant changes. This pattern was still present when L and C were computed as a function of K. A longer path length with a relatively preserved cluster coefficient suggests a loss of complexity and a less optimal organization. The present study provides further support for the presence of "small-world" features in functional brain networks and demonstrates that AD is characterized by a loss of small-world network characteristics. Graph theoretical analysis may be a useful approach to study the complexity of patterns of interrelations between EEG channels.

A unifying explanation of primary generalized seizures through nonlinear brain modeling and bifurcation analysis.

The aim of this paper is to explain critical features of the human primary generalized epilepsies by investigating the dynamical bifurcations of a nonlinear model of the brain's mean field dynamics. The model treats the cortex as a medium for the propagation of waves of electrical activity, incorporating key physiological processes such as propagation delays, membrane physiology, and corticothalamic feedback. Previous analyses have demonstrated its descriptive validity in a wide range of healthy states and yielded specific predictions with regards to seizure phenomena. We show that mapping the structure of the nonlinear bifurcation set predicts a number of crucial dynamic processes, including the onset of periodic and chaotic dynamics as well as multistability. Quantitative study of electrophysiological data supports the validity of these predictions. Hence, we argue that the core electrophysiological and cognitive differences between tonic-clonic and absence seizures are predicted and interrelated by the global bifurcation diagram of the model's dynamics. The present study is the first to present a unifying explanation of these generalized seizures using the bifurcation analysis of a dynamical model of the brain.

A disturbance of nonlinear interdependence in scalp EEG of subjects with first episode schizophrenia.

It has been proposed that schizophrenia arises through a disturbance of coupling between large-scale cortical systems. This "disconnection hypothesis" is tested by applying a measure of dynamical interdependence to scalp EEG data. EEG data were collected from 40 subjects with a first episode of schizophrenia and 40 matched healthy controls. An algorithm for the detection of dynamical interdependence was applied to six pairs of bipolar electrodes in each subject. The topographic organization of the interdependence was calculated and served as the principle measure of cortical integration. The rate of occurrence of dynamical interdependence did not statistically differ between subject groups at any of the sites. However, the topography across the scalp was significantly different between the two groups. Specifically, nonlinear interdependence tended to occur in larger concurrent "clusters" across the scalp in schizophrenia than in the healthy subjects. This disturbance was reflected most strongly in left intrahemispheric coupling and did not differ significantly according to symptomatology. Medication dose and subject arousal were not observed to be confounding factors. The study of dynamical interdependence in scalp EEG data does not support a straightforward interpretation of the disconnection hypothesis-that there is a decrease in the strength of functional coupling between adjacent cortical regions. Rather, it suggests a dysregulation in the organization of dynamical interactions across supraregional brain systems.

Topographic organization of nonlinear interdependence in multichannel human EEG.

This paper investigates the spatial organization of nonlinear interactions between different brain regions in healthy human subjects. This is achieved by studying the topography of nonlinear interdependence in multichannel EEG data, acquired from 40 healthy human subjects at rest. An algorithm for the detection and quantification of nonlinear interdependence is applied to four pairs of bipolar electrode derivations to detect posterior and anterior interhemispheric and left and right intrahemispheric interdependences. Multivariate surrogate data sets are constructed to control for linear coherence and finite sample size. Nonlinear interdependence is shown to occur in a small but statistically robust number of epochs. The occurrence of nonlinear interdependence in any region is correlated with the concurrent presence of nonlinear interdependence in other regions at high levels of significance. The strength, direction and topography of the interdependences are also correlated. For example, posterior interhemispheric interdependence from right-to-left is strongly correlated with right intrahemispheric interdependence from back-to-front. There is a subtle change in these correlations when subjects open their eyes. These results suggest that nonlinear interdependence in the human brain has a specific topographic organization which reflects simple cognitive changes. It sometimes occurs as an isolated phenomenon between two brain regions, but often involves concurrent interdependences between multiple brain regions.

Detection and description of non-linear interdependence in normal multichannel human EEG data.

OBJECTIVES: This study examines human scalp electroencephalographic (EEG) data for evidence of non-linear interdependence between posterior channels. The spectral and phase properties of those epochs of EEG exhibiting non-linear interdependence are studied. METHODS: Scalp EEG data was collected from 40 healthy subjects. A technique for the detection of non-linear interdependence was applied to 2.048 s segments of posterior bipolar electrode data. Amplitude-adjusted phase-randomized surrogate data was used to statistically determine which EEG epochs exhibited non-linear interdependence. RESULTS: Statistically significant evidence of non-linear interactions were evident in 2.9% (eyes open) to 4.8% (eyes closed) of the epochs. In the eyes-open recordings, these epochs exhibited a peak in the spectral and cross-spectral density functions at about 10 Hz. Two types of EEG epochs are evident in the eyes-closed recordings; one type exhibits a peak in the spectral density and cross-spectrum at 8 Hz. The other type has increased spectral and cross-spectral power across faster frequencies. Epochs identified as exhibiting non-linear interdependence display a tendency towards phase interdependencies across and between a broad range of frequencies. CONCLUSIONS: Non-linear interdependence is detectable in a small number of multichannel EEG epochs, and makes a contribution to the alpha rhythm. Non-linear interdependence produces spatially distributed activity that exhibits phase synchronization between oscillations present at different frequencies. The possible physiological significance of these findings are discussed with reference to the dynamical properties of neural systems and the role of synchronous activity in the neocortex.

Nonlinear interdependence in neural systems: motivation, theory, and relevance.

In this article, we motivate models of medium to large-scale neural activity that place an emphasis on the modular nature of neocortical organization and discuss the occurrence of nonlinear interdependence in such models. On the basis of their functional, anatomical, and physiological properties, it is argued that cortical columns may be treated as the basic dynamical modules of cortical systems. Coupling between these columns is introduced to represent sparse long-range cortical connectivity. Thus, neocortical activity can be modeled as an array of weakly coupled dynamical subsystems. The behavior of such systems is represented by dynamical attractors, which may be fixed point, limit cycle, or chaotic in nature. If all the subsystems are perfectly identical, then the state of identical chaotic synchronization is a possible attractor for the array. Following the introduction of parameter variation across the array, such a state is not possible, although two other important nonlinear interdependences--generalized and phase synchronized--are possible. We suggest that an understanding of nonlinear interdependence may assist advances in models of neural activity and neuroscience time series analysis.

Perception of odors by a nonlinear model of the olfactory bulb.

The behavior of the olfactory bulb is modeled as a network of interconnected cells with nonlinear dynamics. External inputs from sensory neurons are introduced as perturbations to subsets of cells within the network. We describe the attractors of the system and show how they can be classified and ordered according to their varying degrees of symmetry. By studying networks of attractors in the system's phase space, it is shown how different perturbations may evoke specific switches between various patterns of behavior. This ensures that different odors, even if present at extremely low concentrations, are able to evoke a specific spatio-temporal behavior in the olfactory bulb, permitting their unique perception. The model incorporates many of the processes proposed to mediate perception, such as the topographic organisation of sensory systems, destabilization of cortex by sensory input and synchronisation between neurons. It is also consistent with the character of the olfactory electroencephalogram.