Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism.

Despite consensus on the neurological nature of autism spectrum disorders (ASD), brain biomarkers remain unknown and diagnosis continues to be based on behavioral criteria. Growing evidence suggests that brain abnormalities in ASD occur at the level of interconnected networks; however, previous attempts using functional connectivity data for diagnostic classification have reached only moderate accuracy. We selected 252 low-motion resting-state functional MRI (rs-fMRI) scans from the Autism Brain Imaging Data Exchange (ABIDE) including typically developing (TD) and ASD participants (n = 126 each), matched for age, non-verbal IQ, and head motion. A matrix of functional connectivities between 220 functionally defined regions of interest was used for diagnostic classification, implementing several machine learning tools. While support vector machines in combination with particle swarm optimization and recursive feature elimination performed modestly (with accuracies for validation datasets <70%), diagnostic classification reached a high accuracy of 91% with random forest (RF), a nonparametric ensemble learning method. Among the 100 most informative features (connectivities), for which this peak accuracy was achieved, participation of somatosensory, default mode, visual, and subcortical regions stood out. Whereas some of these findings were expected, given previous findings of default mode abnormalities and atypical visual functioning in ASD, the prominent role of somatosensory regions was remarkable. The finding of peak accuracy for 100 interregional functional connectivities further suggests that brain biomarkers of ASD may be regionally complex and distributed, rather than localized.

Event related potentials study of aberrations in voice control mechanisms in adults with attention deficit hyperactivity disorder.

OBJECTIVE: The present study was designed to test for neural signs of impulsivity related to voice motor control in young adults with ADHD using EEG recordings in a voice pitch perturbation paradigm. METHODS: Two age-matched groups of young adults were presented with brief pitch shifts of auditory feedback during vocalization. Compensatory behavioral and corresponding bioelectrical brain responses were elicited by the pitch-shifted voice feedback. RESULTS: The analysis of bioelectrical responses showed that the ADHD group had shorter peak latency and onset time of motor-related bioelectrical brain responses as compared to the controls. CONCLUSIONS: These results were interpreted to suggest differences in executive functions between ADHD and control participants. SIGNIFICANCE: We hypothesize that more rapid motor-related bioelectrical responses found in the present study may be a manifestation of impulsiveness in adults with ADHD at the involuntary level of voice control.

Frontal alpha EEG asymmetry before and after behavioral activation treatment for depression.

BACKGROUND: Mid-frontal and mid-lateral (F3/F4 and F7/F8) EEG asymmetry has been associated with motivation and affect. We examined alpha EEG asymmetry in depressed and healthy participants before and after Behavioral Activation treatment for depression; examined the association between alpha EEG asymmetry and motivational systems and affect; and evaluated the utility of alpha EEG asymmetry in predicting remission. METHODS: Depressed (n=37) and healthy participants (n=35) were assessed before and after treatment using a clinical interview, a task to measure baseline EEG, and questionnaires of behavioral activation and inhibition, avoidance, and affect. RESULTS: Alpha EEG asymmetry was significantly higher in depressed than healthy participants at pre-treatment, positively correlated with negative affect and behavioral inhibition, and inversely correlated with lower behavioral activation sensitivity. CONCLUSIONS: Heightened alpha EEG asymmetry in depressed participants was significantly associated with increased behavioral inhibition and negative emotion and was independent of clinical remission.

Mapping repetition suppression of the P50 evoked response to the human cerebral cortex.

OBJECTIVE: The cerebral network subserving repetition suppression (RS) of the P50 auditory evoked response as observed using paired-identical-stimulus (S1-S2) paradigms is not well-described. METHODS: We analyzed S1-S2 data from electrodes placed on the cortices of 64 epilepsy patients. We identified regions with maximal amplitude responses to S1 (i.e., stimulus registration), regions with maximal suppression of responses to S2 relative to S1 (i.e., RS), and regions with no or minimal RS 30-80 ms post stimulation. RESULTS: Several temporal, parietal and cingulate area regions were shown to have significant initial registration activity (i.e., strong P50 response to S1). Moreover, prefrontal, cingulate, and parietal lobe regions not previously proposed to be part of the P50 habituation neural circuitry were found to exhibit significant RS. CONCLUSIONS: The data suggest that the neural network underlying the initial phases of the RS process may include regions not previously thought to be involved like the parietal and cingulate cortexes. In addition, a significant role for the frontal lobe in mediating this function is supported. SIGNIFICANCE: A number of regions of interest are identified through invasive recording that will allow further probing of the RS function using less invasive technology.

Mutual information spectrum for selection of event-related spatial components. Application to eloquent motor cortex mapping.

Spatial component analysis is often used to explore multidimensional time series data whose sources cannot be measured directly. Several methods may be used to decompose the data into a set of spatial components with temporal loadings. Component selection is of crucial importance, and should be supported by objective criteria. In some applications, the use of a well defined component selection criterion may provide for automation of the analysis. In this paper we describe a novel approach for ranking of spatial components calculated from the EEG or MEG data recorded within evoked response paradigm. Our method is called Mutual Information (MI) Spectrum and is based on gauging the amount of MI of spatial component temporal loadings with a synthetically created reference signal. We also describe the appropriate randomization based statistical assessment scheme that can be used for selection of components with statistically significant amount of MI. Using simulated data with realistic trial to trial variations and SNR corresponding to the real recordings we demonstrate the superior performance characteristics of the described MI based measure as compared to a more conventionally used power driven gauge. We also demonstrate the application of the MI Spectrum for the selection of task-related independent components from real MEG data. We show that the MI spectrum allows to identify task-related components reliably in a consistent fashion, yielding stable results even from a small number of trials. We conclude that the proposed method fits naturally the information driven nature of ICA and can be used for routine and automatic ranking of independent components calculated from the functional neuroimaging data collected within event-related paradigms.

A programmable laboratory testbed in support of evaluation of functional brain activation and connectivity.

An important determinant of the value of quantitative neuroimaging studies is the reliability of the derived information, which is a function of the data collection conditions. Near infrared spectroscopy (NIRS) and electroencelphalography are independent sensing domains that are well suited to explore principal elements of the brain's response to neuroactivation, and whose integration supports development of compact, even wearable, systems suitable for use in open environments. In an effort to maximize the translatability and utility of such resources, we have established an experimental laboratory testbed that supports measures and analysis of simulated macroscopic bioelectric and hemodynamic responses of the brain. Principal elements of the testbed include 1) a programmable anthropomorphic head phantom containing a multisignal source array embedded within a matrix that approximates the background optical and bioelectric properties of the brain, 2) integrated translatable headgear that support multimodal studies, and 3) an integrated data analysis environment that supports anatomically based mapping of experiment-derived measures that are directly and not directly observable. Here, we present a description of system components and fabrication, an overview of the analysis environment, and findings from a representative study that document the ability to experimentally validate effective connectivity models based on NIRS tomography.

Mapping repetition suppression of the N100 evoked response to the human cerebral cortex.

BACKGROUND: Repetition suppression (RS) phenomena, such as those observed using paired-identical-stimulus (S1-S2) paradigms, likely reflect adaptive functions such as habituation and, more specifically, sensory gating. METHODS: To better characterize the neural networks underlying RS, we analyzed auditory S1-S2 data from electrodes placed on the cortices of 64 epilepsy patients who were being evaluated for surgical therapy. We identified regions with maximal amplitude responses to S1 (i.e., stimulus registration regions), regions with maximal suppression of responses to S2 relative to S1 (i.e., RS), and regions with no or minimal RS. RESULTS: Auditory perceptual regions, such as the superior temporal gyri, were shown to have significant initial registration activity (i.e., strong response to S1). Several prefrontal, cingulate, and parietal lobe regions were found to exhibit stronger RS than those recorded from the auditory perceptual areas. CONCLUSIONS: The data strongly suggest that the neural network underlying repetition suppression may include regions not previously thought to be involved, such as the parietal and cingulate cortexes. In addition, the data also support the notion that the initial response to stimuli and the ability to suppress the stimuli if repeated are two separate, but likely related, functions.

Generators of the intracranial P50 response in auditory sensory gating.

Clarification of the cortical mechanisms underlying auditory sensory gating may advance our understanding of brain dysfunctions associated with schizophrenia. To this end, data from nine epilepsy patients who participated in an auditory paired-click paradigm during pre-surgical evaluation and had grids of electrodes covering temporal and frontal lobe were analyzed. A distributed source localization approach was applied to the intracranial P50 response and the Gating Difference Wave obtained by subtracting the response to the second stimuli from the response to the first stimuli. Source reconstruction of the P50 showed that the main generators of the response were localized in the temporal lobes. The analysis also suggested that the maximum neuronal activity contributing to the amplitude reduction in the P50 time range (phenomenon of auditory sensory gating) is localized at the frontal lobe. Present findings suggest that while the temporal lobe is the main generator of the P50 component, the frontal lobe seems to be a substantial contributor to the process of sensory gating as observed from scalp recordings.

Randomization-based hypothesis testing from event-related data.

Methods are described for non-parametric significance testing from event-related encephalographic data, using randomization tests. These methods may be applied in both signal space and source space. The methods include within-subject between-condition comparisons, paired and unpaired comparisons, and within-group and between-group comparisons. Test statistics are also derived for comparing the spatial or temporal response patterns, independent of specific changes at individual locations. Novel methods for testing peak-height significance, and also for making map-wide comparisons, are described. These methods have been validated using simulated data.