Models of functional neuroimaging data

Topological Data Analysis Captures Task-Driven fMRI Profiles in Individual Participants: A Classification Pipeline Based on Persistence

BOLD-based fMRI is the most widely used method for studying brain function. The BOLD signal while valuable, is beset with unique vulnerabilities. The most notable of these is the modest signal to noise ratio, and the relatively low temporal and spatial resolution. However, the high dimensional complexity of the BOLD signal also presents unique opportunities for functional discovery. Topological Data Analyses (TDA), a branch of mathematics optimized to search for specific classes of structure within high dimensional data may provide particularly valuable applications. In this investigation, we acquired fMRI data in the anterior cingulate cortex (ACC) using a basic motor control paradigm. Then, for each participant and each of three task conditions, fMRI signals in the ACC were summarized using two methods: a) TDA based methods of persistent homology and persistence landscapes and b) non-TDA based methods using a standard vectorization scheme. Finally, using machine learning (with support vector classifiers), classification accuracy of TDA and non-TDA vectorized data was tested across participants. In each participant, TDA-based classification out-performed the non-TDA based counterpart, suggesting that our TDA analytic pipeline better characterized task- and condition-induced structure in fMRI data in the ACC. Our results emphasize the value of TDA in characterizing task- and condition-induced structure in regional fMRI signals. In addition to providing our analytical tools for other users to emulate, we also discuss the unique role that TDA-based methods can play in the study of individual differences in the structure of functional brain signals in the healthy and the clinical brain.

Functional Connectivity Evoked by Orofacial Tactile Perception of Velocity

The cortical representations of orofacial pneumotactile stimulation involve complex neuronal networks, which are still unknown. This study aims to identify the characteristics of functional connectivity (FC) evoked by three different saltatory velocities over the perioral and buccal surface of the lower face using functional magnetic resonance imaging in twenty neurotypical adults. Our results showed a velocity of 25 cm/s evoked stronger connection strength between the right dorsolateral prefrontal cortex and the right thalamus than a velocity of 5 cm/s. The decreased FC between the right secondary somatosensory cortex and right posterior parietal cortex for 5-cm/s velocity versus all three velocities delivered simultaneously (“All ON”) and the increased FC between the right thalamus and bilateral secondary somatosensory cortex for 65 cm/s vs “All ON” indicated that the right secondary somatosensory cortex might play a role in the orofacial tactile perception of velocity. Our results have also shown different patterns of FC for each seed (bilateral primary and secondary somatosensory cortex) at various velocity contrasts (5 vs 25 cm/s, 5 vs 65 cm/s, and 25 vs 65 cm/s). The similarities and differences of FC among three velocities shed light on the neuronal networks encoding the orofacial tactile perception of velocity.

Gradient theories of brain activation: A novel application to studying the parental brain

PURPOSE OF REVIEW

Parental brain research primarily employs general-linear-model-based (GLM-based) analyses to assess blood-oxygenation-level-dependent responses to infant auditory and visual cues, reporting common responses in shared cortical and subcortical structures. However, this approach does not reveal intermixed neural substrates related to different sensory modalities. We consider this notion in studying the parental brain.

RECENT FINDINGS

Spatial independent component analysis (sICA) has been used to separate mixed source signals from overlapping functional networks. We explore relative differences between GLM-based analysis and sICA as applied to an fMRI dataset acquired from women while they listened to infant cries or viewed infant sad faces.

SUMMARY

There is growing appreciation for the value of moving beyond GLM-based analyses to consider brain functional organization as continuous, distributive, and overlapping gradients of neural substrates related to different sensory modalities. Preliminary findings suggest sICA can be applied to the study of the parental brain.

Cognitive Profiles and Functional Connectivity in First-Episode Schizophrenia Spectrum Disorders - Linking Behavioral and Neuronal Data

The character of cognitive deficit in schizophrenia is not clear due to the heterogeneity in research results. In heterogeneous conditions, the cluster solution allows the classification of individuals based on profiles. Our aim was to examine the cognitive profiles of first-episode schizophrenia spectrum disorder (FES) subjects based on cluster analysis, and to correlate these profiles with clinical variables and resting state brain connectivity, as measured with magnetic resonance imaging. A total of 67 FES subjects were assessed with a neuropsychological test battery and on clinical variables. The results of the cognitive domains were cluster analyzed. In addition, functional connectivity was calculated using ROI-to-ROI analysis with four groups: Three groups were defined based on the cluster analysis of cognitive performance and a control group with a normal cognitive performance. The connectivity was compared between the patient clusters and controls. We found different cognitive profiles based on three clusters: Cluster 1: decline in the attention, working memory/flexibility, and verbal memory domains. Cluster 2: decline in the verbal memory domain and above average performance in the attention domain. Cluster 3: generalized and severe deficit in all of the cognitive domains. FES diagnoses were distributed among all of the clusters. Cluster comparisons in neural connectivity also showed differences between the groups. Cluster 1 showed both hyperconnectivity between the cerebellum and precentral gyrus, the salience network (SN) (insula cortex), and fronto-parietal network (FPN) as well as between the PreCG and SN (insula cortex) and hypoconnectivity between the default mode network (DMN) and seeds of SN [insula and supramarginal gyrus (SMG)]; Cluster 2 showed hyperconnectivity between the DMN and cerebellum, SN (insula) and precentral gyrus, and FPN and IFG; Cluster 3 showed hypoconnectivity between the DMN and SN (insula) and SN (SMG) and pallidum. The cluster solution confirms the prevalence of a cognitive decline with different patterns of cognitive performance, and different levels of severity in FES. Moreover, separate behavioral cognitive subsets can be linked to patterns of brain functional connectivity.

Self-perception of mental illness, and subjective and objective cognitive functioning in people with schizophrenia

Background: People with schizophrenia may have a negative self-perception of mental illness. However, the relationship between the self-perception of illness and subjective and objective cognitive functioning remains unclear. Method: Thirty-seven people with schizophrenia were enrolled in the study group. All subjects completed self-reported self-perception of mental illness questionnaires (Birchwood Insight Scale [BIS], Internalized Stigma of Mental Illness [ISMI]), subjective cognitive functioning (Scale to Investigate Cognition in Schizophrenia [SSTICS]) and objective cognitive functioning (Luria Nebraska Neuropsychological Battery [LNNB]). Results: Spearman's rank analysis showed that awareness of illness (domain of insight) was positively associated with most domains of SSTICS and ISMI. Total insight was positively correlated with alienation (domain of ISMI).  Need for treatment (domain of insight) was negatively correlated with stigma resistance (domain of ISMI). Awareness of symptoms (domain of insight) and total insight were negatively associated with receptive speech and arithmetic (LNNB subtest), respectively. ISMI was positively correlated with most domains of SSTICS, but not with LNNB. The group with good insight had higher scores in attention (domain of SSTICS) and total SSTICS and alienation, stereotype endorsement, social withdrawal (domains of ISMI) and total ISMI than the group with poor insight. The group with mild to moderate internalized stigma had higher scores in explicit memory, attention, language, praxia (domains of SSTICS) and total SSTICS than the group with no internalized stigma. Conclusion: We identified that awareness of illness (domain of insight), internalized stigma were significantly associated with most domains of SSTICS, but not with LNNB. Total insight and awareness of symptoms (domain of insight) were significantly associated with receptive speech and arithmetic (LNNB subtest), respectively. Schizophrenia with higher insight or more internalized stigma reported more subjective cognitive impairment. Future studies with larger samples involving follow up are necessary to verify our findings and extend the applicability.

Theoretical Modeling of Cognitive Dysfunction in Schizophrenia by Means of Errors and Corresponding Brain Networks

The current evidence of cognitive disturbances and brain alterations in schizophrenia does not provide the plausible explanation of the underlying mechanisms. Neuropsychological studies outlined the cognitive profile of patients with schizophrenia, that embodied the substantial disturbances in perceptual and motor processes, spatial functions, verbal and non-verbal memory, processing speed and executive functioning. Standardized scoring in the majority of the neurocognitive tests renders the index scores or the achievement indicating the severity of the cognitive impairment rather than the actual performance by means of errors. At the same time, the quantitative evaluation may lead to the situation when two patients with the same index score of the particular cognitive test, demonstrate qualitatively different performances. This may support the view why test paradigms that habitually incorporate different cognitive variables associate weakly, reflecting an ambiguity in the interpretation of noted cognitive constructs. With minor exceptions, cognitive functions are not attributed to the localized activity but eventuate from the coordinated activity in the generally dispersed brain networks. Functional neuroimaging has progressively explored the connectivity in the brain networks in the absence of the specific task and during the task processing. The spatio-temporal fluctuations of the activity of the brain areas detected in the resting state and being highly reproducible in numerous studies, resemble the activation and communication patterns during the task performance. Relatedly, the activation in the specific brain regions oftentimes is attributed to a number of cognitive processes. Given the complex organization of the cognitive functions, it becomes crucial to designate the roles of the brain networks in relation to the specific cognitive functions. One possible approach is to identify the commonalities of the deficits across the number of cognitive tests or, common errors in the various tests and identify their common "denominators" in the brain networks. The qualitative characterization of cognitive performance might be beneficial in addressing diffuse cognitive alterations presumably caused by the dysconnectivity of the distributed brain networks. Therefore, in the review, we use this approach in the description of standardized tests in the scope of potential errors in patients with schizophrenia with a subsequent reference to the brain networks.

Neural and Genetic Correlates of the Social Sharing of Happiness

Happiness is regarded as one of the most fundamental human goals. Given recent reports that positive feelings are contagious (e.g., the presence of a happy person enhances others' happiness) because of the human ability to empathize (i.e., sharing emotions), empathic ability may be a key factor in increasing one's own subjective level of happiness. Based on previous studies indicating that a single nucleotide polymorphism in the serotonin 2A receptor gene [HTR2A rs6311 guanine (G) vs. adenine (A)] is associated with sensitivity to emotional stimuli and several mental disorders such as depression, we predicted that the polymorphism might be associated with the effect of sharing happiness. To elucidate the neural and genetic correlates of the effect of sharing happiness, we first performed functional magnetic resonance imaging (fMRI) during a “happy feelings” evocation task (emotional event imagination task), during which we manipulated the valence of the imagined event (positive, neutral, or negative), as well as the presence of a friend experiencing a positive-valence event (presence or absence). We recruited young adult women for this fMRI study because empathic ability may be higher in women than in men. Participants felt happier (p < 0.01) and the mentalizing/theory-of-mind network, which spans the medial prefrontal cortex, temporoparietal junction, temporal poles, and precuneus, was significantly more active (p < 0.05) in the presence condition than in the absence condition regardless of event valence. Moreover, participants with the GG (p < 0.01) and AG (p < 0.05) genotypes of HTR2A experienced happier feelings as well as greater activation of a part of the mentalizing/theory-of-mind network (p < 0.05) during empathy for happiness (neutral/presence condition) than those with the AA genotype. In a follow-up study with a vignette-based questionnaire conducted in a relatively large sample, male and female participants were presented with the same imagined events wherein their valence and the presence of a friend were manipulated. Results showed genetic differences in happiness-related empathy regardless of sex (p < 0.05). Findings suggest that HTR2A polymorphisms are associated with the effect of sharing happiness by modulating the activity of the mentalizing/theory-of-mind network.

Luria revisited: cognitive research in schizophrenia, past implications and future challenges

Contemporary psychiatry is becoming more biologically oriented in the attempt to elicit a biological rationale of mental diseases. Although mental disorders comprise mostly functional abnormalities, there is a substantial overlap between neurology and psychiatry in addressing cognitive disturbances. In schizophrenia, the presence of cognitive impairment prior to the onset of psychosis and early after its manifestation suggests that some neurocognitive abnormalities precede the onset of psychosis and may represent a trait marker. These cognitive alterations may arise from functional disconnectivity, as no significant brain damage has been found. In this review we aim to revise A.R. Luria's systematic approach used in the neuropsychological evaluation of cognitive functions, which was primarily applied in patients with neurological disorders and in the cognitive evaluation in schizophrenia and other related disorders. As proposed by Luria, cognitive processes, associated with higher cortical functions, may represent functional systems that are not localized in narrow, circumscribed areas of the brain, but occur among groups of concertedly working brain structures, each of which makes its own particular contribution to the organization of the functional system. Current developments in neuroscience provide evidence of functional connectivity in the brain. Therefore, Luria's approach may serve as a frame of reference for the analysis and interpretation of cognitive functions in general and their abnormalities in schizophrenia in particular. Having said that, modern technology, as well as experimental evidence, may help us to understand the brain better and lead us towards creating a new classification of cognitive functions. In schizophrenia research, multidisciplinary approaches must be utilized to address specific cognitive alterations. The relationships among the components of cognitive functions derived from the functional connectivity of the brain may provide an insight into cognitive machinery.

Intra- and inter-frequency brain network structure in health and schizophrenia

Empirical studies over the past two decades have provided support for the hypothesis that schizophrenia is characterized by altered connectivity patterns in functional brain networks. These alterations have been proposed as genetically mediated diagnostic biomarkers and are thought to underlie altered cognitive functions such as working memory. However, the nature of this dysconnectivity remains far from understood. In this study, we perform an extensive analysis of functional connectivity patterns extracted from MEG data in 14 subjects with schizophrenia and 14 healthy controls during a 2-back working memory task. We investigate uni-, bi- and multivariate properties of sensor time series by computing wavelet entropy of and correlation between time series, and by constructing binary networks of functional connectivity both within and between classical frequency bands ([Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]). Networks are based on the mutual information between wavelet time series, and estimated for each trial window separately, enabling us to consider both network topology and network dynamics. We observed significant decreases in time series entropy and significant increases in functional connectivity in the schizophrenia group in comparison to the healthy controls and identified an inverse relationship between these measures across both subjects and sensors that varied over frequency bands and was more pronounced in controls than in patients. The topological organization of connectivity was altered in schizophrenia specifically in high frequency [Formula: see text] and [Formula: see text] band networks as well as in the [Formula: see text]-[Formula: see text] cross-frequency networks. Network topology varied over trials to a greater extent in patients than in controls, suggesting disease-associated alterations in dynamic network properties of brain function. Our results identify signatures of aberrant neurophysiological behavior in schizophrenia across uni-, bi- and multivariate scales and lay the groundwork for further clinical studies that might lead to the discovery of new intermediate phenotypes.

State-dependent differences between functional and effective connectivity of the human cortical motor system

Neural processing is based on interactions between functionally specialized areas that can be described in terms of functional or effective connectivity. Functional connectivity is often assessed by task-free, resting-state functional magnetic resonance imaging (fMRI), whereas effective connectivity is usually estimated from task-based fMRI time-series. To investigate whether different connectivity approaches assess similar network topologies in the same subjects, we scanned 36 right-handed volunteers with resting-state fMRI followed by active-state fMRI involving a hand movement task. Time-series information was extracted from identical locations defined from individual activation maxima derived from the motor task. Dynamic causal modeling (DCM) was applied to the motor task time-series to estimate endogenous and context-dependent effective connectivity. In addition, functional connectivity was computed for both the rest and the motor task condition by means of inter-regional time-series correlations. At the group-level, we found strong interactions between the motor areas of interest in all three connectivity analyses. However, although the sample size warranted 90% power to detect correlations of medium effect size, resting-state functional connectivity was only weakly correlated with both task-based functional and task-based effective connectivity estimates for corresponding region-pairs. By contrast, task-based functional connectivity showed strong positive correlations with DCM effective connectivity parameters. In conclusion, resting-state and task-based connectivity reflect different components of functional integration that particularly depend on the functional state in which the subject is being scanned. Therefore, resting-state fMRI and DCM should be used as complementary measures when assessing functional brain networks.

The CNS and bladder dysfunction

The brain's role in the development and maintenance of bladder control is critical, although its precise role in patient-reported complaints such as urgency and urine leakage is unknown. Functional brain imaging studies have advanced our knowledge of brain activity during the micturition cycle, showing multiple neuronal circuits involved as parts of a ‘brain-bladder control network.’ Yet, new advances need to be made in order to incorporate this knowledge into existing models of neuroanatomy and of clinical syndromes of bladder dysfunction and related clinical practice. This short article explains why and how brain imaging methods are poised to achieve that goal and decode the role of the brain in widely prevalent clinical conditions related to bladder dysfunction.