Broadband neurophysiological abnormalities in the medial prefrontal region of the default‐mode network in adults with ADHD

Wilson, Tony W.; Franzen, John D.; Heinrichs‐Graham, Elizabeth; White, Matthew L.; Knott, Nichole L.; Wetzel, Martin W.

doi:10.1002/hbm.21459

Cited by 54 publications

(41 citation statements)

References 40 publications

(47 reference statements)

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“…The use of pre-defined ROIs guarantees a result, circumventing the issue altogether. Graph theoretic MEG studies of the DMN have also typically used predefined ROIs to compare connectivity (Franzen et al, 2013; Van Dellen et al, 2013; Wilson et al, 2013; You et al, 2013), rather than using direct data-driven extraction of the network. Recent work looking at DMN connectivity has suggested that non-stationarity in the MEG signal may be the cause of the lack of interhemispheric connectivity in MEG resting state studies.…”

Section: Discussionmentioning

confidence: 99%

Graph theoretical analysis of resting-state MEG data: Identifying interhemispheric connectivity and the default mode

2014

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Interhemispheric connectivity with resting state MEG has been elusive, and demonstration of the default mode network (DMN) yet more challenging. Recent seed-based MEG analyses have shown interhemispheric connectivity using power envelope correlations. The purpose of this study is to compare graph theoretic maps of brain connectivity generated using MEG with and without signal leakage correction to evaluate for the presence of interhemispheric connectivity. Eight minutes of resting state eyes-open MEG data were obtained in 22 normal male subjects enrolled in an IRB-approved study (ages 16–18). Data were processed using an in-house automated MEG processing pipeline and projected into standard (MNI) source space at 7 mm resolution using a scalar beamformer. Mean beta-band amplitude was sampled at 2.5 second epochs from the source space time series. Leakage correction was performed in the time domain of the source space beam-formed signal prior to amplitude transformation. Graph theoretic voxel-wise source space correlation connectivity analysis was performed for leakage-corrected and uncorrected data. Degree maps were thresholded across subjects for the top 20% of connected nodes to identify hubs. Additional degree maps for sensory, visual, motor, and temporal regions were generated to identify interhemispheric connectivity using laterality indices. Hubs for the uncorrected MEG networks were predominantly symmetric and midline, bearing some resemblance to fMRI networks. These included the cingulate cortex, bilateral inferior frontal lobes, bilateral hippocampal formations and bilateral cerebellar hemispheres. These uncorrected networks however, demonstrated little to no interhemispheric connectivity using the ROI-based degree maps. Leakage corrected MEG data identified the DMN, with hubs in the posterior cingulate and biparietal areas. These corrected networks demonstrated robust interhemispheric connectivity for the ROI-based degree maps. Graph theoretic analysis of MEG resting state data without signal leakage correction can demonstrate symmetric networks with some resemblance to fMRI networks. These networks however, are an artifact of high local correlation from signal leakage and lack interhemispheric connectivity. Following signal leakage correction, MEG hubs emerge in the DMN, with strong interhemispheric connectivity.

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Section: Discussionmentioning

confidence: 99%

Graph theoretical analysis of resting-state MEG data: Identifying interhemispheric connectivity and the default mode

2014

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“…Neural activity estimates were extracted from these regions using an inverse spatial filtering approach (Franzen et al, 2013; Wilson et al, 2013b; see also Scherg et al, 2002) and these data were transformed into the frequency domain using Fourier analysis. We focused on the amplitude of oscillatory activity in the same beta band that was identified in the motor task through permutation testing.…”

Section: Methodsmentioning

confidence: 99%

Circadian modulation of motor-related beta oscillatory responses

Wilson

Heinrichs‐Graham

Becker³

2014

NeuroImage

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Previous electrophysiological investigations have evaluated movement-related beta (14–28 Hz) oscillatory activity in healthy participants. These studies have described an abrupt decrease in beta activity that starts before movement onset, and a sharp increase in beta power that peaks after movement termination. These neural responses have been respectively termed the event-related beta desynchronization or pre-movement beta ERD, and the post-movement beta rebound (PMBR). Previous studies have shown that a variety of movement parameters and demographic factors (e.g., age) modulate the amplitude of these oscillatory responses, and in the current study we evaluated whether the amplitudes follow a biological temporal rhythm (e.g., circadian), as it is known that spontaneous beta levels increase from morning to afternoon in some brain areas. To this end, we used magnetoencephalography (MEG) to evaluate oscillatory activity during a right hand finger-tapping task in four participants who were recorded at three different times (09:00, 12:00, 16:00) on three consecutive days (i.e., 36 total MEG sessions). All MEG data were corrected for head motion and examined in the time-frequency domain using beamforming methods. We found a significant linear increase in beta ERD amplitude from 09:00 to 16:00 hours in the left precentral gyrus, left premotor cortices, left supplementary motor area (SMA), and the right precentral and postcentral gyri. In contrast, the amplitude of the PMBR was very steady across the day in all brain regions except the left SMA, which exhibited a linear increase from morning to afternoon. Finally, beta levels during the baseline period also increased from 09:00 to 16:00 in most regions of the cortical sensorimotor network. These data show that both the pre-movement beta ERD and spontaneous beta levels strongly increase from morning to afternoon in the motor cortices, which may indicate that the amplitude of the beta ERD response is determined by the spontaneous beta level during the motor planning period.

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“…For example, differences in MEG functional connectivity compared to healthy controls has been reported for Alzheimer's Disease (Sorg et al, 2009; Stam et al, 2009), Attention-Deficit/Hyperactivity Disorder (Wilson et al, 2013), and epilepsy (Sakurai et al, 2010). Furthermore, acquired brain injury such as a stroke can radically alter functional connectivity patterns (Castellanos et al, 2010, 2011; Gerloff and Hallett, 2010).…”

Section: Functional Network As Assessed With Electrophysiologymentioning

confidence: 99%

The contribution of electrophysiology to functional connectivity mapping

et al. 2013

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A powerful way to probe brain function is to assess the relationship between simultaneous changes in activity across different parts of the brain. In recent years, the temporal activity correlation between brain areas has frequently been taken as a measure of their functional connections. Evaluating ‘functional connectivity’ in this way is particularly popular in the fMRI community, but has also drawn interest among electrophysiologists. Like hemodynamic fluctuations observed with fMRI, electrophysiological signals display significant temporal fluctuations, even in the absence of a stimulus. These neural fluctuations exhibit correlational structure over a wide range of spatial and temporal scales. Initial evidence suggests that certain aspects of this correlational structure bear a high correspondence to so-called functional networks defined using fMRI. The growing family of methods to study activity covariation, combined with the diverse neural mechanisms that contribute to the spontaneous fluctuations, have somewhat blurred the operational concept of functional connectivity. What is clear is that spontaneous activity is a conspicuous, energy-consuming feature of the brain. Given its prominence and its practical applications for the functional connectivity mapping of brain networks, it is of increasing importance that we understand its neural origins as well as its contribution to normal brain function.

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Broadband neurophysiological abnormalities in the medial prefrontal region of the default‐mode network in adults with ADHD

Cited by 54 publications

References 40 publications

Graph theoretical analysis of resting-state MEG data: Identifying interhemispheric connectivity and the default mode

Graph theoretical analysis of resting-state MEG data: Identifying interhemispheric connectivity and the default mode

Circadian modulation of motor-related beta oscillatory responses

The contribution of electrophysiology to functional connectivity mapping

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