Modeling Brain Responses

When mind and body align: examining the role of cross-modal congruency in conscious representations of happy facial expressions

This study explored how congruency between facial mimicry and observed expressions affects the stability of conscious facial expression representations. Focusing on the congruency effect between proprioceptive/sensorimotor signals and visual stimuli for happy expressions, participants underwent a binocular rivalry task displaying neutral and happy faces. Mimicry was either facilitated with a chopstick or left unrestricted. Key metrics included Initial Percept (bias indicator), Onset Resolution Time (time from onset to Initial Percept), and Cumulative Time (content stabilization measure). Results indicated that mimicry manipulation significantly impacted Cumulative Time for happy faces, highlighting the importance of congruent mimicry in stabilizing conscious awareness of facial expressions. This supports embodied cognition models, showing the integration of proprioceptive information significantly biases conscious visual perception of facial expressions.

Medial Prefrontal Cortex Dysfunction Mediates Working Memory Deficits in Patients With Schizophrenia

Background

Schizophrenia (SCZ) is marked by working memory (WM) deficits, which predict poor functional outcome. While most functional magnetic resonance imaging studies of WM in SCZ have focused on the dorsolateral prefrontal cortex (PFC), some recent work suggests that the medial PFC (mPFC) may play a role. We investigated whether task-evoked mPFC deactivation is associated with WM performance and whether it mediates deficits in SCZ. In addition, we investigated associations between mPFC deactivation and cortical dopamine release.

Methods

Patients with SCZ (n = 41) and healthy control participants (HCs) (n = 40) performed a visual object n-back task during functional magnetic resonance imaging. Dopamine release capacity in mPFC was quantified with [11C]FLB457 in a subset of participants (9 SCZ, 14 HCs) using an amphetamine challenge. Correlations between task-evoked deactivation and performance were assessed in mPFC and dorsolateral PFC masks and were further examined for relationships with diagnosis and dopamine release.

Results

mPFC deactivation was associated with WM task performance, but dorsolateral PFC activation was not. Deactivation in the mPFC was reduced in patients with SCZ relative to HCs and mediated the relationship between diagnosis and WM performance. In addition, mPFC deactivation was significantly and inversely associated with dopamine release capacity across groups and in HCs alone, but not in patients.

Conclusions

Reduced WM task-evoked mPFC deactivation is a mediator of, and potential substrate for, WM impairment in SCZ, although our study design does not rule out the possibility that these findings could relate to cognition in general rather than WM specifically. We further present preliminary evidence of an inverse association between deactivation during WM tasks and dopamine release capacity in the mPFC.

Focal neural perturbations reshape low-dimensional trajectories of brain activity supporting cognitive performance

The emergence of distributed patterns of neural activity supporting brain functions and behavior can be understood by study of the brain's low-dimensional topology. Functional neuroimaging demonstrates that brain activity linked to adaptive behavior is constrained to low-dimensional manifolds. In human participants, we tested whether these low-dimensional constraints preserve working memory performance following local neuronal perturbations. We combined multi-session functional magnetic resonance imaging, non-invasive transcranial magnetic stimulation (TMS), and methods translated from the fields of complex systems and computational biology to assess the functional link between changes in local neural activity and the reshaping of task-related low dimensional trajectories of brain activity. We show that specific reconfigurations of low-dimensional trajectories of brain activity sustain effective working memory performance following TMS manipulation of local activity on, but not off, the space traversed by these trajectories. We highlight an association between the multi-scale changes in brain activity underpinning cognitive function.

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.

Pros and Cons of Using the Informed Basis Set to Account for Hemodynamic Response Variability with Developmental Data

Conventional analysis of functional magnetic resonance imaging (fMRI) data using the general linear model (GLM) employs a neural model convolved with a canonical hemodynamic response function (HRF) peaking 5 s after stimulation. Incorporation of a further basis function, namely the canonical HRF temporal derivative, accounts for delays in the hemodynamic response to neural activity. A population that may benefit from this flexible approach is children whose hemodynamic response is not yet mature. Here, we examined the effects of using the set based on the canonical HRF plus its temporal derivative on both first- and second-level GLM analyses, through simulations and using developmental data (an fMRI dataset on proprioceptive mapping in children and adults). Simulations of delayed fMRI first-level data emphasized the benefit of carrying forward to the second-level a derivative boost that combines derivative and nonderivative beta estimates. In the experimental data, second-level analysis using a paired t-test showed increased mean amplitude estimate (i.e., increased group contrast mean) in several brain regions related to proprioceptive processing when using the derivative boost compared to using only the nonderivative term. This was true especially in children. However, carrying forward to the second-level the individual derivative boosts had adverse consequences on random-effects analysis that implemented one-sample t-test, yielding increased between-subject variance, thus affecting group-level statistic. Boosted data also presented a lower level of smoothness that had implication for the detection of group average activation. Imposing soft constraints on the derivative boost by limiting the time-to-peak range of the modeled response within a specified range (i.e., 4–6 s) mitigated these issues. These findings support the notion that there are pros and cons to using the informed basis set with developmental data.

Successful Working Memory Processes and Cerebellum in an Elderly Sample: A Neuropsychological and fMRI Study

Background

Imaging studies help to understand the evolution of key cognitive processes related to aging, such as working memory (WM). This study aimed to test three hypotheses in older adults. First, that the brain activation pattern associated to WM processes in elderly during successful low load tasks is located in posterior sensory and associative areas; second, that the prefrontal and parietal cortex and basal ganglia should be more active during high-demand tasks; third, that cerebellar activations are related to high-demand cognitive tasks and have a specific lateralization depending on the condition.

Methods

We used a neuropsychological assessment with functional magnetic resonance imaging and a core N-back paradigm design that was maintained across the combination of four conditions of stimuli and two memory loads in a sample of twenty elderly subjects.

Results

During low-loads, activations were located in the visual ventral network. In high loads, there was an involvement of the basal ganglia and cerebellum in addition to the frontal and parietal cortices. Moreover, we detected an executive control role of the cerebellum in a relatively symmetric fronto-parietal network. Nevertheless, this network showed a predominantly left lateralization in parietal regions associated presumably with an overuse of verbal storage strategies. The differential activations between conditions were stimuli-dependent and were located in sensory areas.

Conclusion

Successful WM processes in the elderly population are accompanied by an activation pattern that involves cerebellar regions working together with a fronto-parietal network.

Gray matter atrophy is associated with functional connectivity reorganization during the Paced Auditory Serial Addition Test (PASAT) execution in Multiple Sclerosis (MS)

BACKGROUND AND PURPOSE

We explored the relationship between gray matter atrophy and reorganization of functional connectivity in multiple sclerosis patients during execution of the Paced Auditory Serial Addition Test (PASAT).

MATERIALS AND METHODS

Seventeen patients and 15 healthy controls were selected for the study. Atrophy was determined using voxel-based morphometry, and atrophy-related connectivity changes were assessed using psychophysiological interaction analysis. Group differences, and correlations with PASAT performance and radiological variables were also examined.

RESULTS

Gray matter atrophy in MS patients was circumscribed to the bilateral posterior cingulate gyrus/precuneus. Compared with controls, patients showed stronger connectivity between the left posterior cingulate gyrus/precuneus, and the left middle temporal gyrus and left cerebellum. A regression analysis in controls showed a negative correlation between PASAT scores and functional connectivity between: (1) the left posterior cingulate gyrus/precuneus, and left pre/postcentral gyri and left occipital gyrus, and (2) the right posterior cingulate gyrus/precuneus, and bilateral cerebellum and left pre/postcentral gyri. Patients showed a negative correlation between brain parenchymal fraction and functional connectivity between the left posterior cingulate gyrus/precuneus and left cerebellum.

CONCLUSION

Patients with early MS and little brain damage presented more connectivity during PASAT execution, which may be interpreted as compensatory processes that help preserve cognitive functions.

The functional anatomy of schizophrenia: A dynamic causal modeling study of predictive coding

This paper tests the hypothesis that patients with schizophrenia have a deficit in selectively attending to predictable events. We used dynamic causal modeling (DCM) of electrophysiological responses - to predictable and unpredictable visual targets - to quantify the effective connectivity within and between cortical sources in the visual hierarchy in 25 schizophrenia patients and 25 age-matched controls. We found evidence for marked differences between normal subjects and schizophrenia patients in the strength of extrinsic backward connections from higher hierarchical levels to lower levels within the visual system. In addition, we show that not only do schizophrenia subjects have abnormal connectivity but also that they fail to adjust or optimize this connectivity when events can be predicted. Thus, the differential intrinsic recurrent connectivity observed during processing of predictable versus unpredictable targets was markedly attenuated in schizophrenia patients compared with controls, suggesting a failure to modulate the sensitivity of neurons responsible for passing sensory information of prediction errors up the visual cortical hierarchy. The findings support the proposed role of abnormal connectivity in the neuropathology and pathophysiology of schizophrenia.

Analysis of "task-positive" and "task-negative" functional networks during the performance of the Symbol Digit Modalities Test in patients at presentation with clinically isolated syndrome suggestive of multiple sclerosis

An abnormal pattern of brain activations has been shown in patients with multiple sclerosis during the performance of several cognitive tasks. The aim of this study is to investigate abnormalities of the patterns of activation/deactivation in the functional networks related to "task-positive" and "task-negative" events during the execution of the Symbol Digit Modalities Test (SDMT) in patients with clinically isolated syndromes (CIS) and preserved cognitive abilities. Eighteen CIS patients within 3 months from their first clinical attack and 15 healthy controls (HC) underwent neuropsychological assessment and performed an adapted functional magnetic resonance imaging (fMRI) version of the SDMT. "Task-positive" responses to task execution and "task-negative" activity of the default mode network were compared between groups. A regression analysis was performed to investigate the correlation between fMRI results and T2 lesion load (T2 LL) and brain atrophy. Neuropsychological performance did not differ between groups. Compared to HC, CIS patients exhibited an enhanced deactivation of the "task-negative" network at the level of the posterior cingulate cortex, whereas no differences between groups were found when the patterns of "task-positive" events were compared. A regression analysis detected a correlation (p < 0.001,r ranging from 0.62 to 0.73) between T2 LL and "task-positive" activations of areas that are part of the attention network, comprising the anterior cingulate gyrus, left prefrontal gyrus and inferior parietal lobe. No correlation was found between patterns of functional modifications and brain atrophy. CIS patients experience an enhanced pattern of brain deactivations during cognitive performances, which might contribute to their normal neuropsychological status.

EEG-fMRI Methods for the Study of Brain Networks during Sleep

Modern neuroimaging methods may provide unique insights into the mechanism and role of sleep, as well as into particular mechanisms of brain function in general. Many of the recent neuroimaging studies have used concurrent EEG and fMRI, which present unique technical challenges ranging from the difficulty of inducing sleep in the MRI environment to appropriate instrumentation and data processing methods to obtain artifact free data. In addition, the use of EEG-fMRI during sleep leads to unique data interpretation issues, as common approaches developed for the analysis of task-evoked activity do not apply to sleep. Reviewed are a variety of statistical approaches that can be used to characterize brain activity from fMRI data acquired during sleep, with an emphasis on approaches that investigate the presence of correlated activity between brain regions. Each of these approaches has advantages and disadvantages that must be considered in concert with the theoretical questions of interest. Specifically, fundamental theories of sleep control and function should be considered when designing these studies and when choosing the associated statistical approaches. For example, the notion that local brain activity during sleep may be triggered by local, use-dependent activity during wakefulness may be tested by analyzing sleep networks as statistically independent components. Alternatively, the involvement of regions in more global processes such as arousal may be investigated with correlation analysis.

Functional magnetic resonance imaging correlates of cognitive performance in patients with a clinically isolated syndrome suggestive of multiple sclerosis at presentation: an activation and connectivity study

Background/Objective: To assess whether abnormalities on functional magnetic resonance imaging (fMRI) are related to cognitive function in patients at presentation with clinically isolated syndrome (CIS) suggestive of multiple sclerosis.

Methods: Eighteen patients with CIS and 15 healthy controls (HCs) performed an adapted fMRI version of the Paced Auditory Serial Addition Test (PASAT). According to their PASAT performance, CIS patients were divided into two groups: 10 with a low PASAT performance (<1 SD from the mean value of HCs) were considered ‘cognitive impairment’ (CI); eight patients were defined as ‘cognitively preserved’ (CP). Between-group differences in the patterns of brain activations and effective connectivity were assessed.

Results: During PASAT, compared to HCs, CIS patients showed increased activations of the bilateral inferior parietal lobe (IPL), bilateral precuneus, bilateral middle frontal gyrus (MFG), left anterior cingulate cortex (ACC), left claustrum, right thalamus and right caudate nucleus. When CIS patients were analyzed, the CI group had a more significant activation of the bilateral IPL than HCs and CP patients. Compared to CP patients, they also had more significant recruitment of the right superior parietal lobe, right cerebellum, left MFG and left ACC. The analysis of effective connectivity showed stronger connections between several regions of the right hemisphere involved in working memory function in CI patients versus CP and HC.

Conclusions: During performance of the PASAT, CIS patients show abnormalities in the patterns of cortical recruitment and connectivity related to the level of their cognitive impairment.

Brain dynamics sustaining rapid rule extraction from speech

Language acquisition is a complex process that requires the synergic involvement of different cognitive functions, which include extracting and storing the words of the language and their embedded rules for progressive acquisition of grammatical information. As has been shown in other fields that study learning processes, synchronization mechanisms between neuronal assemblies might have a key role during language learning. In particular, studying these dynamics may help uncover whether different oscillatory patterns sustain more item-based learning of words and rule-based learning from speech input. Therefore, we tracked the modulation of oscillatory neural activity during the initial exposure to an artificial language, which contained embedded rules. We analyzed both spectral power variations, as a measure of local neuronal ensemble synchronization, as well as phase coherence patterns, as an index of the long-range coordination of these local groups of neurons. Synchronized activity in the gamma band (20-40 Hz), previously reported to be related to the engagement of selective attention, showed a clear dissociation of local power and phase coherence between distant regions. In this frequency range, local synchrony characterized the subjects who were focused on word identification and was accompanied by increased coherence in the theta band (4-8 Hz). Only those subjects who were able to learn the embedded rules showed increased gamma band phase coherence between frontal, temporal, and parietal regions.

Optical brain imaging reveals general auditory and language-specific processing in early infant development

This study uses near-infrared spectroscopy in young infants in order to elucidate the nature of functional cerebral processing for speech. Previous imaging studies of infants' speech perception revealed left-lateralized responses to native language. However, it is unclear if these activations were due to language per se rather than to some low-level acoustic correlate of spoken language. Here we compare native (L1) and non-native (L2) languages with 3 different nonspeech conditions including emotional voices, monkey calls, and phase scrambled sounds that provide more stringent controls. Hemodynamic responses to these stimuli were measured in the temporal areas of Japanese 4 month-olds. The results show clear left-lateralized responses to speech, prominently to L1, as opposed to various activation patterns in the nonspeech conditions. Furthermore, implementing a new analysis method designed for infants, we discovered a slower hemodynamic time course in awake infants. Our results are largely explained by signal-driven auditory processing. However, stronger activations to L1 than to L2 indicate a language-specific neural factor that modulates these responses. This study is the first to discover a significantly higher sensitivity to L1 in 4 month-olds and reveals a neural precursor of the functional specialization for the higher cognitive network.

Quantifying auditory event-related responses in multichannel human intracranial recordings

Multichannel intracranial recordings are used increasingly to study the functional organization of human cortex. Intracranial recordings of event-related activity, or electrocorticography (ECoG), are based on high density electrode arrays implanted directly over cortex, combining good temporal and spatial resolution. Developing appropriate statistical methods for analyzing event-related responses in these high dimensional ECoG datasets remains a major challenge for clinical and systems neuroscience. We present a novel methodological framework that combines complementary, existing methods adapted for statistical analysis of auditory event-related responses in multichannel ECoG recordings. This analytic framework integrates single-channel (time-domain, time–frequency) and multichannel analyses of event-related ECoG activity to determine statistically significant evoked responses, induced spectral responses, and effective (causal) connectivity. Implementation of this quantitative approach is illustrated using multichannel ECoG data from recent studies of auditory processing in patients with epilepsy. Methods described include a time–frequency matching pursuit algorithm adapted for modeling brief, transient cortical spectral responses to sound, and a recently developed method for estimating effective connectivity using multivariate autoregressive modeling to measure brief event-related changes in multichannel functional interactions. A semi-automated spatial normalization method for comparing intracranial electrode locations across patients is also described. The individual methods presented are published and readily accessible. We discuss the benefits of integrating multiple complementary methods in a unified and comprehensive quantitative approach. Methodological considerations in the analysis of multichannel ECoG data, including corrections for multiple comparisons are discussed, as well as remaining challenges in the development of new statistical approaches.

Differential parametric modulation of self-relatedness and emotions in different brain regions

Our sense of self is strongly colored by emotions although at the same time we are well able to distinguish affect and self. Using functional magnetic resonance imaging, we here tested for the differential effects of self-relatedness and emotion dimensions (valence, intensity) on parametric modulation of neural activity during perception of emotional stimuli. We observed opposite parametric modulation of self-relatedness and emotion dimensions in the dorsomedial prefrontal cortex and the ventral striatum/nucleus accumbens, whereas neural activity in subcortical regions (tectum, right amygdala, hypothalamus) was modulated by self-relatedness and emotion dimensions in the same direction. In sum, our results demonstrate that self-relatedness is closely linked to emotion dimensions of valence and intensity in many lower subcortical brain regions involved in basic emotional systems and, at the same time, distinct from them in higher cortical regions that mediate cognitive processes necessary for becoming aware of one's self, for example self-consciousness. Hum

Identifying neural drivers with functional MRI: an electrophysiological validation

Whether functional magnetic resonance imaging (fMRI) allows the identification of neural drivers remains an open question of particular importance to refine physiological and neuropsychological models of the brain, and/or to understand neurophysiopathology. Here, in a rat model of absence epilepsy showing spontaneous spike-and-wave discharges originating from the first somatosensory cortex (S1BF), we performed simultaneous electroencephalographic (EEG) and fMRI measurements, and subsequent intracerebral EEG (iEEG) recordings in regions strongly activated in fMRI (S1BF, thalamus, and striatum). fMRI connectivity was determined from fMRI time series directly and from hidden state variables using a measure of Granger causality and Dynamic Causal Modelling that relates synaptic activity to fMRI. fMRI connectivity was compared to directed functional coupling estimated from iEEG using asymmetry in generalised synchronisation metrics. The neural driver of spike-and-wave discharges was estimated in S1BF from iEEG, and from fMRI only when hemodynamic effects were explicitly removed. Functional connectivity analysis applied directly on fMRI signals failed because hemodynamics varied between regions, rendering temporal precedence irrelevant. This paper provides the first experimental substantiation of the theoretical possibility to improve interregional coupling estimation from hidden neural states of fMRI. As such, it has important implications for future studies on brain connectivity using functional neuroimaging.

Our understanding of how the brain works relies on the development of neuropsychological models, which describe how brain activity is coordinated among different regions during the execution of a given task. Knowing the directionality of information transfer between connected regions, and in particular distinguishing neural drivers, or the source of forward connections in the brain, from other brain regions, is critical to refine models of the brain. However, whether functional magnetic resonance imaging (fMRI), the most common technique for imaging brain function, allows one to identify neural drivers remains an open question. Here, we used a rat model of absence epilepsy, a form of nonconvulsive epilepsy that occurs during childhood in humans, showing spontaneous spike-and-wave discharges (nonconvulsive seizures) originating from the first somatosensory cortex, to validate several functional connectivity measures derived from fMRI. Standard techniques estimating interactions directly from fMRI data failed because blood flow dynamics varied between regions. However, we were able to identify the neural driver of spike-and-wave discharges when hemodynamic effects were explicitly removed using appropriate modelling. This study thus provides the first experimental substantiation of the theoretical possibility to improve interregional coupling estimation from hidden neural states of fMRI. As such, it has important implications for future studies on connectivity in the functional neuroimaging literature.

Neural long-range interactions can be distinguished from hemodynamic confounds in functional magnetic resonance imaging using new data analysis techniques that will allow experimental validation of models of brain function.