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Understanding the connection between the brain’s structural connectivity and its functional connectivity is of immense interest in computational neuroscience. Although some studies have suggested that whole brain functional connectivity is shaped by the underlying structure, the rule by which anatomy constraints brain dynamics remains an open question. In this work, we introduce a computational framework that identifies a joint subspace of eigenmodes for both functional and structural connectomes. We found that a small number of those eigenmodes are sufficient to reconstruct functional connectivity from the structural connectome, thus serving as low-dimensional basis function set. We then develop an algorithm that can estimate the functional eigen spectrum in this joint space from the structural eigen spectrum. By concurrently estimating the joint eigenmodes and the functional eigen spectrum, we can reconstruct a given subject’s functional connectivity from their structural connectome. We perform elaborate experiments and demonstrate that the proposed algorithm for estimating functional connectivity from the structural connectome using joint space eigenmodes gives competitive performance as compared to the existing benchmark methods with better interpretability.

Background Individuals with either deletion or duplication of the BP4–BP5 segment of chromosome 16p11.2 have varied behavioral phenotypes that may include autistic features, mild to moderate intellectual disability, and/or language impairment. However, the neurophysiological correlates of auditory language discrimination processing in individuals with 16p11.2 deletion and 16p11.2 duplication have not been investigated. Methods Magnetoencephalography was used to measure magnetic mismatch fields (MMFs) arising from the left and right superior temporal gyrus during an auditory oddball paradigm with vowel stimuli (/a/ and /u/) in children and adolescents with 16p11.2 deletion or 16p11.2 duplication and in typically developing peers. One hundred twenty-eight participants ranging from 7 to 17 years of age were included in the final analysis (typically developing: n = 61, 12.08 ± 2.50 years of age; 16p11.2 deletion: n = 45, 11.28 ± 2.51 years of age; and 16p11.2 duplication: n = 22, 10.73 ± 2.49 years of age). Results Delayed MMF latencies were found in both 16p11.2 deletion and 16p11.2 duplication groups compared with typically developing subjects. In addition, these delayed MMF latencies were associated with language and cognitive ability, with prolonged latency predicting greater impairment. Conclusions Our findings suggest that auditory MMF response delays are associated with clinical severity of language and cognitive impairment in individuals with either 16p11.2 deletion or 16p11.2 duplication, indicating a correlate of their shared/overlapping behavioral phenotype (and not a correlate of gene dosage).

Accurate reconstruction of the spatio-temporal dynamics of event-related cortical oscillations across human brain regions is an important problem in functional brain imaging and human cognitive neuroscience with magnetoencephalography (MEG) and electroencephalography (EEG). The problem is challenging not only in terms of localization of complex source configurations from sensor measurements with unknown noise and interference but also for reconstruction of transient event-related time-frequency dynamics of cortical oscillations. We recently proposed a robust empirical Bayesian algorithm for simultaneous reconstruction of complex brain source activity and noise covariance, in the context of evoked and resting-state data. In this paper, we expand upon this empirical Bayesian framework for optimal reconstruction of event-related time-frequency dynamics of regional cortical oscillations, referred to as time-frequency Champagne (TFC). This framework enables imaging of five-dimensional (space, time, and frequency) event-related brain activity from M/EEG data, and can be viewed as a time-frequency optimized adaptive Bayesian beamformer. We evaluate TFC in both simulations and several real datasets, with comparisons to benchmark standards - variants of time-frequency optimized adaptive beamformers (TFBF) as well as the sLORETA algorithm. In simulations, we demonstrate several advantages in estimating time-frequency cortical oscillatory dynamics compared to benchmarks. With real MEG data, we demonstrate across many datasets that the proposed approach is robust to highly correlated brain activity and low SNR data, and is able to accurately reconstruct cortical dynamics with data from just a few epochs.

Human brain organoids replicate much of the cellular diversity and developmental anatomy of the human brain. However, neurophysiology within organoids remains relatively under-explored. With high-density CMOS microelectrode arrays and shank electrodes, we probed broadband and three-dimensional activity of human brain organoids. From spiking activity, we estimated a directed functional connectivity graph of neural network activity which showed a large number of weak functional connections enmeshed within a network skeleton of significantly fewer strong connections. Increasing the intrinsic inhibitory tone with a benzodiazepine suppressed the network skeleton of the functional network. We further demonstrate that human brain organoids have self-organized neuronal assemblies of sufficient size, cellular orientation, and functional connectivity to generate local field potentials from their collective transmembrane currents that phase-lock to spiking activity. These results point to the potential of brain organoids for the study of neuropsychiatric diseases, drug mechanisms, and the effects of external stimuli upon neuronal networks.

In this paper, we present a novel hierarchical multiscale Bayesian algorithm for electromagnetic brain imaging using magnetoencephalography (MEG) and electroencephalography (EEG). In particular, we present a solution to the source reconstruction problem for sources that vary in spatial extent. We define sensor data measurements using a generative probabilistic graphical model that is hierarchical across spatial scales of brain regions and voxels. We then derive a novel Bayesian algorithm for probabilistic inference with this graphical model. This algorithm enables robust reconstruction of sources that have different spatial extent, from spatially contiguous clusters of dipoles to isolated dipolar sources. We compare the new algorithm with several representative benchmarks on both simulated and real brain activities. The source locations and the correct estimation of source time courses used for the simulated data are chosen to test the performance on challenging source configurations. In simulations, performance of the novel algorithm shows superiority to several existing benchmark algorithms. We also demonstrate that the new algorithm is more robust to correlated brain activity present in real MEG and EEG data and is able to resolve distinct and functionally relevant brain areas with real MEG and EEG datasets.

Electromagnetic (E/MEG) brain source imaging involves challenging problems that make it particularly difficult to estimate both the locations and extents of extended sources. In this study, we propose a new method called Variation Sparse Source Imaging based on Conditional Mean of the posterior (VSSI-CM), which is built upon a Bayesian framework, to reconstruct extended E/MEG generators. Based on the proposed framework, VSSI-CM can employ various spatial priors (e.g., the Laplace prior) to explore sparseness of current sources in transform domains (e.g., the variation transform in this study). Considering the complexity of posterior density in the estimated sources, we propose using the posterior mean instead of the typical maximum a posterior (MAP) estimate as a more accurate inverse solution. The posterior mean is obtained by fitting an approximated Gaussian distribution to the intractable true posterior distribution. An efficient double-loop algorithm is also proposed using convex analysis skills. Validation using synthetic and human experimental data sets indicates that VSSI-CM outperforms the well-studied L2-norm methods (i.e., sLORETA and dSPM) and the sparse constrained methods that explore sparseness in the original source domain. The estimates from VSSI-CM are also more accurate than that from MAP.

We examined the relationship between white matter microstructure in schizophrenia using diffusion tensor imaging (DTI) and cognitive improvements induced by 70 hours (~16 weeks) of cognitive training. We measured anatomical connectivity in 48 patients with schizophrenia (SZ) and 28 healthy control participants (HC) at baseline, and then examined the relationship between anatomical connectivity at baseline and training-induced cognitive gains in 30 SZ who performed diffusion imaging after completing 70 hours of training. Compared with healthy control participants, individuals with schizophrenia showed reduced white matter integrity at baseline, as indexed by fractional anisotropy metrics, in bilateral posterior corona radiata, bilateral retrolenticular internal capsules, bilateral posterior thalamic radiation, left anterior corona radiata, left superior longitudinal fasciculus, left sagittal stratum, right cerebral peduncle and the genu and splenium of the corpus callosum. After training, schizophrenia participants showed significant gains in attention/vigilance, speed of processing, verbal learning, visual learning and executive functioning. White matter integrity within the right fronto-occipital fasciculus predicted training-induced improvements in attention/vigilance, while white matter integrity within the right corticospinal tract and bilateral medial lemnisci predicted cognitive training-induced improvements in executive functioning, areas that did not show white matter tract deficits at baseline. These findings suggest that preserved white matter integrity connecting long-range prefrontal-thalamic-sensorimotor areas may be an important determinant for training-induced neurocognitive plasticity.

Estimating the locations and spatial extents of brain sources poses a long-standing challenge for electroencephalography and magnetoencephalography (E/MEG) source imaging. In the present work, a novel source imaging method, Bayesian Electromagnetic Spatio-Temporal Imaging of Extended Sources (BESTIES), which is built upon a Bayesian framework that determines the spatio-temporal smoothness of source activities in a fully data-driven fashion, is proposed to address this challenge. In particular, a Markov Random Field (MRF), which can precisely capture local cortical interactions, is employed to characterize the spatial smoothness of source activities, the temporal dynamics of which are modeled by a set of temporal basis functions (TBFs). Crucially, all of the unknowns in the MRF and TBF models are learned from the data. To accomplish model inference efficiently on high-resolution source spaces, a scalable algorithm is developed to approximate the posterior distribution of the source activities, which is based on the variational Bayesian inference and convex analysis. The performance of BESTIES is assessed using both simulated and actual human E/MEG data. Compared with L2-norm constrained methods, BESTIES is superior in reconstructing extended sources with less spatial diffusion and less localization error. By virtue of the MRF, BESTIES also overcomes the drawback of over-focal estimates in sparse constrained methods.

Navigated transcranial magnetic stimulation (nTMS) is a non-invasive technique for pre-surgical motor and language mapping in patients with brain lesions. This study examines the safety and tolerability of nTMS in a large, multi-center cohort of neurosurgical patients.Functional mapping with monopulse and repetitive nTMS was performed in 733 patients. In this cohort, 57% of patients had left-sided tumors, 50% had frontal tumors, and 50% had seizures secondary to the lesion. Side effects and pain intensity related to the procedure were documented.Patients undergoing monopulse stimulation underwent an average of 490 pulses while those undergoing repetitive stimulation received an average of 2268 pulses. During monopulse stimulation, 5.1% reported discomfort (VAS 1-3), and 0.4% reported pain (VAS>3). During repetitive stimulation, 23.4% reported discomfort and 69.5% reported pain. No seizures or other adverse events were observed.nTMS is safe and well-tolerated in neurosurgical patients. Clinicians should consider expanding nTMS to patients with frequent seizures, but more evaluation is necessary to evaluate this risk fully.nTMS is safe and well-tolerated, even in neurosurgical patients with persistent occasional seizure secondary to a lesion. It should be considered in any patient with a lesion in a presumed peri-eloquent or eloquent brain region.

We investigated whether intensive computerized cognitive training in schizophrenia could improve working memory performance and increase signal efficiency of associated middle frontal gyri (MFG) circuits in a functionally meaningful manner. Thirty schizophrenia participants and 13 healthy comparison participants underwent fMRI scanning during a letter N-back working memory task. Schizophrenia participants were then randomly assigned to either 80 hours (16 weeks) of cognitive training or a computer games control condition. After this intervention, participants completed a second fMRI N-back scanning session. At baseline, during 2-back working memory trials, healthy participants showed the largest and most significant activation in bilateral MFG, which correlated with task performance. Schizophrenia participants showed impaired working memory, hypoactivation in left MFG, and no correlation between bilateral MFG signal and task performance. After training, schizophrenia participants improved their 2-back working memory performance and showed increased activation in left MFG. They also demonstrated a significant association between enhanced task performance and right MFG signal, similar to healthy participants. Both task performance and brain activity in right MFG after training predicted better generalized working memory at 6-month follow-up. Furthermore, task performance and brain activity within bilateral MFG predicted better occupational functioning at 6-month follow-up. No such findings were observed in the computer games control participants. Working memory impairments in schizophrenia and its underlying neural correlates in MFG can be improved by intensive computerized cognitive training; these improvements generalize beyond the trained task and are associated with enduring effects on cognition and functioning 6 months after the intervention.

Auditory feedback is used to monitor and correct for errors in speech production, and one of the clearest demonstrations of this is the pitch perturbation reflex. During ongoing phonation, speakers respond rapidly to shifts of the pitch of their auditory feedback, altering their pitch production to oppose the direction of the applied pitch shift. In this study, we examine the timing of activity within a network of brain regions thought to be involved in mediating this behavior. To isolate auditory feedback processing relevant for motor control of speech, we used magnetoencephalography (MEG) to compare neural responses to speech onset and to transient (400 ms) pitch feedback perturbations during speaking with responses to identical acoustic stimuli during passive listening. We found overlapping, but distinct bilateral cortical networks involved in monitoring speech onset and feedback alterations in ongoing speech. Responses to speech onset during speaking were suppressed in bilateral auditory and left ventral supramarginal gyrus/posterior superior temporal sulcus (vSMG/pSTS). In contrast, during pitch perturbations, activity was enhanced in bilateral vSMG/pSTS, bilateral premotor cortex, right primary auditory cortex, and left higher order auditory cortex. We also found speaking-induced delays in responses to both unaltered and altered speech in bilateral primary and secondary auditory regions, left vSMG/pSTS and right premotor cortex. The network dynamics reveal the cortical processing involved in both detecting the speech error and updating the motor plan to create the new pitch output. These results implicate vSMG/pSTS as critical in both monitoring auditory feedback and initiating rapid compensation to feedback errors.

Introduction Accurate delineation of epileptogenic zone(EZ) is crucial in patients with medically refractory epilepsy (MRE) warranting surgery and at least 33% will have seizure recurrence post-surgery. Intracranial EEG studies had showed that high frequency oscillations (HFO; 80–200 Hz) can accurately localize the EZ and patients had better post-surgical seizure freedom, if brain regions generating HFOs were resected out. However, none of the noninvasive studies evaluated the clinical utility of HFOs. So, we investigated epileptic HFOs source imaging to delineate the presurgical EZ and predict the seizure freedom. Methods Fifty-two patients (M: F = 37:15; age = 23 ± 8.9 years; disease duration 11.4 ± 7.7 years) with MRE were longitudinally recruited and studied with MEG. Each interictal epileptic discharge were marked by assessing spatial topography. Subject specific semi-realistic head model and a 3D grid voxels of 10 mm resolution was constructed. Source reconstruction was performed with frequency beamformer, by attenuating the source power at a specified location, subject to unit-gain constraint. Gain matrix was determined and cross-spectral density matrix on a Fourier transformed data was calculated at 7 distinct frequency bands; 8–14 Hz, 14–30 Hz, 1–30 Hz, 30–54 Hz, 55–80 Hz, 30–80 Hz, and 80–200 Hz. From the available clinical data, EZ was defined, respective surgery was performed and surgical outcome was assessed. The congruency of source construction with the EZ and surgically resected cortex was established at four levels, for each band & strength was quantified by kappa statistic. Sensitivity, positive predictive value (PPV) and accuracy was evaluated. Source localization error (SLE) was determined on the basis of minimum euclidean distance between the margin of reconstructed sources with the volume of the resected cortex. Results With surgery, 44 (84.6%) patients had good (Engel-I) and 8 (15.4%) patients had poor (Engel-II-IV) clinical outcome at 22.1 ± 10 months follow up. Compared to other frequency bands, interictal HFO imaging accurately delineated the epileptogenic cortex in 80.3% (k = 0.44 ± 0.12) of the patients, with 80.8% sensitivity, 100% PPV, and 80.32% accuracy. The highest possible concordance rate of 78.8% (k = 0.46 ± 0.1) with the surgically resected cortical volume, was observed for the reconstructed HFO sources. HFO source reconstruction did correlate with the seizure freedom (p Conclusion To date, this is the first ever largest prospective & noninvasive (MEG) study to investigate the clinical role of the HFOs, where HFOs precisely delineated the EZ & predicted the patients who might achieve seizure freedom with surgery, emerging as a key noninvasive epilepsy biomarker.

Understanding neural network dysfunction in neurodegenerative disease is imperative to effectively develop network-modulating therapies. In Alzheimer’s disease (AD), cognitive decline associates with deficits in resting-state functional connectivity of diffuse brain networks. The goal of the current study was to test whether specific cognitive impairments in AD spectrum correlate with reduced functional connectivity of distinct brain regions. We recorded resting-state functional connectivity of alpha-band activity in 27 patients with AD spectrum − 22 patients with probable AD (5 logopenic variant primary progressive aphasia, 7 posterior cortical atrophy, and 10 early-onset amnestic/dysexecutive AD) and 5 patients with mild cognitive impairment due to AD. We used magnetoencephalographic imaging (MEGI) to perform an unbiased search for regions where patterns of functional connectivity correlated with disease severity and cognitive performance. Functional connectivity measured the strength of coherence between a given region and the rest of the brain. Decreased neural connectivity of multiple brain regions including the right posterior perisylvian region and left middle frontal cortex correlated with a higher degree of disease severity. Deficits in executive control and episodic memory correlated with reduced functional connectivity of the left frontal cortex, whereas visuospatial impairments correlated with reduced functional connectivity of the left inferior parietal cortex. Our findings indicate that reductions in region-specific alpha-band resting-state functional connectivity are strongly correlated with, and might contribute to, specific cognitive deficits in AD spectrum. In the future, MEGI functional connectivity could be an important biomarker to map and follow defective networks in the early stages of AD., Highlights • Magnetoencephalographic imaging (MEGI) measures brain functional connectivity. • We investigated MEGIalpha-band connectivity in a cohort with Alzheimer’s disease spectrum. • Decreased connectivity of multiple brain regions correlates with disease severity. • Decreased connectivity of focal brain regions correlates with cognitive deficits. • MEGI is a novel, unbiased approach to map neural network defects in dementia.

Survivors post stroke commonly have upper limb impairments. Patients can drive neural reorganization, brain recovery and return of function with task specific repetitive training (TSRT). Fifteen community independent stroke survivors (25e75 years, >6 months post stroke, Upper Limb Fugl Meyer [ULFM] scores 16e39) participated in this randomized feasibility study to compare outcomes of upper limb TSRT guided by a robotic orthosis (bilateral or unilateral) or a physical therapist. After 6 weeks of training (18 h), across all subjects, there were significant improvements in depression, flexibility, strength, tone, pain and voluntary movement (ULFM) (p < 0.05; effect sizes 0.49e3.53). Each training group significantly improved ULFM scores and range of motion without significant group differences. Virtual or actual TSRT performed with a robotic orthosis or a physical therapist significantly reduced arm impairments around the shoulder and elbow without significant gains in fine motor hand control, activities of daily living or independence.

Task-specific focal hand dystonia (tspFHD) is a movement disorder diagnosed in individuals performing repetitive hand behaviors. The extent to which processing anomalies in primary sensory cortex extend to other regions or across the two hemispheres is presently unclear.In response to low/high rate and novel tactile stimuli on the affected and unaffected hands, magnetoencephalography (MEG) was used to elaborate activity timing and amplitude in the primary somatosensory (S1) and secondary somatosensory/parietal ventral (S2/PV) cortices. MEG and clinical performance measures were collected from 13 patients and matched controls.Compared to controls, subjects with tspFHD had increased response amplitude in S2/PV bilaterally in response to high rate and novel stimuli. Subjects with tspFHD also showed increased response latency (low rate, novel) of the affected digits in contralateral S1. For high rate, subjects with tspFHD showed increased response latency in ipsilateral S1 and S2/PV bilaterally. Activation differences correlated with functional sensory deficits (predicting a latency shift in S1), motor speed and muscle strength.There are objective differences in the amplitude and timing of activity for both hands across contralateral and ipsilateral somatosensory cortex in patients with tspFHD.Knowledge of cortical processing abnormalities across S1 and S2/PV in dystonia should be applied towards the development of learning-based sensorimotor interventions.

Functional connectivity (FC) between brain regions is thought to be central to the way in which the brain processes information. Abnormal connectivity is thought to be implicated in a number of diseases. The ability to study FC is therefore a key goal for neuroimaging. Functional connectivity (fc) MRI has become a popular tool to make connectivity measurements but the technique is limited by its indirect nature. A multimodal approach is therefore an attractive means to investigate the electrodynamic mechanisms underlying hemodynamic connectivity. In this paper, we investigate resting state FC using fcMRI and magnetoencephalography (MEG). In fcMRI, we exploit the advantages afforded by ultra high magnetic field. In MEG we apply envelope correlation and coherence techniques to source space projected MEG signals. We show that beamforming provides an excellent means to measure FC in source space using MEG data. However, care must be taken when interpreting these measurements since cross talk between voxels in source space can potentially lead to spurious connectivity and this must be taken into account in all studies of this type. We show good spatial agreement between FC measured independently using MEG and fcMRI; FC between sensorimotor cortices was observed using both modalities, with the best spatial agreement when MEG data are filtered into the β band. This finding helps to reduce the potential confounds associated with each modality alone: while it helps reduce the uncertainties in spatial patterns generated by MEG (brought about by the ill posed inverse problem), addition of electrodynamic metric confirms the neural basis of fcMRI measurements. Finally, we show that multiple MEG based FC metrics allow the potential to move beyond what is possible using fcMRI, and investigate the nature of electrodynamic connectivity. Our results extend those from previous studies and add weight to the argument that neural oscillations are intimately related to functional connectivity and the BOLD response., Research highlights ► The utility of beamforming as a source space projection algorithm for use with functional connectivity (FC) measurements is explored. ► Simulations are described and prove to be a robust methodology to eliminate spurious FC from source space MEG measurements. ► The spatial signature of resting state FC between left and right sensorimotor cortices can be measured independently using MEG and fMRI. ► Excellent agreement between motor cortex FC measured using both MEG and fcMRI. ► Multiple MEG FC metrics exploit the direct nature of MEG.

Introduction Specificity of ictal high-frequency oscillations (HFOs) in identifying epileptogenic abnormality is significant, compared to the spikes and interictal HFOs. The objectives of the study were to detect and to localize ictal HFOs by magnetoencephalography (MEG) for identifying the seizure onset zone (SOZ), evaluate the cortical excitability from preictal to ictal transition, and establish HFO concordance rates with other modalities and postsurgical resection. Methods Sixty-seven patients with drug-resistant epilepsy had at least 1 spontaneous seizure each during MEG acquisition, and analysis was carried out on 20 seizures from 20 patients. Ictal MEG data were bandpass filtered (80–200 Hz) to visualize, review, and analyze the HFOs co-occurring with ictal spikes. Source montages were generated on both hemispheres, mean fast Fourier transform was computed on virtual time series for determining the preictal to ictal spectral power transition, and source reconstruction was performed with sLORETA and beamformers. The concordance rates of ictal MEG HFOs (SOZ) was estimated with 4 reference epileptogenic regions. Results In each subject, transient bursts of high-frequency oscillatory cycles, distinct from the background activity, were observed in the periictal continuum. Time–frequency analysis showed significant spectral power surge (85–160 Hz) during ictal state (P .05) for each subgroup and at each source montage. HFO source localization was consistent between algorithms (k = 0.8570 ± 0.138), with presumed epileptogenic zone (EZ) comparable to other modalities. In patients who underwent surgery (n = 6), MEG HFO SOZ was concordant with the presumed EZ Conclusion HFOs could reliably be detected in the MEG periictal state, and its sources were accurately localized in almost 95% of the patients with medically refractory epilepsy. During preictal to ictal transition, HFOs exhibited dynamic augmentation in intrinsic epileptogenicity. Spatial overlap of ictal HFO sources was consistent with EZ determinants including the presumed EZ and the surgical resection area.

Objective To evaluate the ability of MEG to detect medial temporal spikes in patients with known medial temporal lobe epilepsy (MTLE) and to use magnetic source imaging (MSI) with equivalent current dipoles to examine localization and orientation of spikes and their relation to surgical outcome. Methods We prospectively obtained MSI on a total of 25 patients previously diagnosed with intractable MTLE. MEG was recorded with a 275 channel whole-head system with simultaneous 21-channel scalp EEG during inpatient admission one day prior to surgical resection. The patients’ surgical outcomes were classified based on one-year follow-up after surgery. Results Nineteen of the 22 patients (86.4%) had interictal spikes during the EEG and MEG recordings. Thirteen of 19 patients (68.4%) demonstrated unilateral temporal dipoles ipsilateral to the site of surgery. Among these patients, five (38.5%) patients had horizontal dipoles, one (7.7%) patient had vertical dipoles, and seven (53.8%) patients had both horizontal and vertical dipoles. Sixty percent of patients with non-localizing ictal scalp EEG had well-localized spikes on MSI ipsilateral to the side of surgery and 66.7% of patients with non-localizing MRI had well-localized spikes on MSI ipsilateral to the side of surgery. Concordance between MSI localization and the side of lobectomy was not associated with a likelihood of an excellent postsurgical outcome. Conclusions MSI can detect medial temporal spikes. It may provide important localizing information in patients with MTLE, especially when MRI and/or ictal scalp EEG are not localizing. Significance This study demonstrates that MSI has a good ability to detect interictal spikes from mesial temporal structures.

The synchronous brain activity measured via MEG (or EEG) can be interpreted as arising from a collection (possibly large) of current dipoles or sources located throughout the cortex. Estimating the number, location, and time course of these sources remains a challenging task, one that is significantly compounded by the effects of source correlations and unknown orientations and by the presence of interference from spontaneous brain activity, sensor noise, and other artifacts. This paper derives an empirical Bayesian method for addressing each of these issues in a principled fashion. The resulting algorithm guarantees descent of a cost function uniquely designed to handle unknown orientations and arbitrary correlations. Robust interference suppression is also easily incorporated. In a restricted setting, the proposed method is shown to produce theoretically zero reconstruction error estimating multiple dipoles even in the presence of strong correlations and unknown orientations, unlike a variety of existing Bayesian localization methods or common signal processing techniques such as beamforming and sLORETA. Empirical results on both simulated and real data sets verify the efficacy of this approach.

Advances in structural and functional imaging have provided both scientists and clinicians with information about the neural mechanisms underlying focal hand dystonia (FHd), a motor disorder associated with aberrant posturing and patterns muscle contraction specific to movements of the hand. Consistent with the hypothesis that focal hand dystonia is the result of reorganization in cortical fields, studies in neuroimaging have confirmed alterations in the topography and response properties of somatosensory and motor areas of the brain. Non-invasive stimulation of these regions also demonstrates that focal hand dystonia may be due to reductions in inhibition between competing sensory and motor representations. Compromises in neuroanatomical structure, such as white-matter density and gray matter volume, have also been identified through neuroimaging methods. These advances in neuroimaging have provided clinicians with an expanded understanding of the changes in the brain that contribute to focal hand dystonia. These findings should provide a foundation for the development of retraining paradigms focused on reversing overlapping sensory representations and interactions between brain regions in patients with hand dystonia. Continued collaborations between health professionals that treat focal dystonia and research scientists that examine the brain using neuroimaging tools are imperative for answering difficult questions about patients with specific movement disorders.

The study of human cognition, and preoperative functional brain mapping, are facilitated through the use of magnetoencephalography (MEG). However, the noise present in such recordings is significant relative to the signals of interest. To circumvent this issue multiple trials are performed for the same task and an ensemble average is performed to increase the signal-to-noise/interference ratio (SNIR). Unfortunately, large numbers of trials (100-500) are required to achieve a sufficiently large SNIR. This paper describes a simple denoising technique which employs spatial averaging to potentially reduce the number of required trials. The N trials from each of the 274 channels are first averaged. The 274 averaged channel estimates are then temporally low passed filtered, using a zero-phase filter. Finally, a spatial average is performed, where each channel is assigned the average value of a number of the most similar channels, including itself. The most similar channels are identified using correlation. The technique is applied to an auditory and somatosensory evoked response data set. A gain of approximately 1-1.5dB is observed over low-pass filtering alone.

Intracranial electroencephalography (iEEG) is clinically indicated for medically refractory epilepsy and is a promising approach for developing neural prosthetics. These recordings also provide valuable data for cognitive neuroscience research. Accurate localization of iEEG electrodes is essential for evaluating specific brain regions underlying the electrodes that indicate normal or pathological activity, as well as for relating research findings to neuroimaging and lesion studies. However, electrodes are frequently tucked underneath the edge of a craniotomy, inserted via a burr hole, or placed deep within the brain, where their locations cannot be verified visually or with neuronavigational systems. We show that one existing method, registration of postimplant computed tomography (CT) with preoperative magnetic resonance imaging (MRI), can result in errors exceeding 1 cm. We present a novel method for localizing iEEG electrodes using routinely acquired surgical photographs, X-ray radiographs, and magnetic resonance imaging scans. Known control points are used to compute projective transforms that link the different image sets, ultimately allowing hidden electrodes to be localized, in addition to refining the location of manually registered visible electrodes. As the technique does not require any calibration between the different image modalities, it can be applied to existing image databases. The final result is a set of electrode positions on the patient’s rendered MRI yielding locations relative to sulcal and gyral landmarks on individual anatomy, as well as MNI coordinates. We demonstrate the results of our method in eight epilepsy patients implanted with electrode grids spanning the left hemisphere.

We present two related probabilistic methods for neural source reconstruction from MEG/EEG data that reduce effects of interference, noise, and correlated sources. Both methods localize source activity using a linear mixture of temporal basis functions (TBFs) learned from the data. In contrast to existing methods that use predetermined TBFs, we compute TBFs from data using a graphical factor analysis based model [Nagarajan, S.S., Attias, H.T., Hild, K.E., Sekihara, K., 2007a. A probabilistic algorithm for robust interference suppression in bioelectromagnetic sensor data. Stat Med 26, 3886–3910], which separates evoked or event-related source activity from ongoing spontaneous background brain activity. Both algorithms compute an optimal weighting of these TBFs at each voxel to provide a spatiotemporal map of activity across the brain and a source image map from the likelihood of a dipole source at each voxel. We explicitly model, with two different robust parameterizations, the contribution from signals outside a voxel of interest. The two models differ in a trade-off of computational speed versus accuracy of learning the unknown interference contributions. Performance in simulations and real data, both with large noise and interference and/or correlated sources, demonstrates significant improvement over existing source localization methods.

We have developed a novel probabilistic model that estimates neural source activity measured by MEG and EEG data while suppressing the effect of interference and noise sources. The model estimates contributions to sensor data from evoked sources, interference sources and sensor noise using Bayesian methods and by exploiting knowledge about their timing and spatial covariance properties. Full posterior distributions are computed rather than just the MAP estimates. In simulation, the algorithm can accurately localize and estimate the time courses of several simultaneously active dipoles, with rotating or fixed orientation, at noise levels typical for averaged MEG data. The algorithm even performs reasonably at noise levels typical of an average of just a few trials. The algorithm is superior to beamforming techniques, which we show to be an approximation to our graphical model, in estimation of temporally correlated sources. Success of this algorithm using MEG data for localizing bilateral auditory cortex, low-SNR somatosensory activations, and for localizing an epileptic spike source are also demonstrated.

Fetal magnetocardiographic sensor measurements are contaminated by undesired environmental and biological signals, such as the maternal cardiac signal. Several methods have been used in an attempt to extract the fetal cardiac signal from these data, which are based on, e.g., the presumed quasi-periodicity of the maternal cardiac signal or the presumed statistical independence between the fetal cardiac signal and interfering signals. Recently a different type of method for extracting signals from noisy data has been introduced. This probabilistic method, known as partitioned factor analysis (PFA), assumes that the data can be partitioned into periods of source inactivity and source activity. PFA was originally developed for stimulus-evoked, trial-averaged encephalographic data, for which the partitions are known in advance. Here we show how to use PFA for extracting the fetal cardiac signal from cardiographic data, for which the partitions must be determined from the data. In addition, we show that PFA can be used even when the partitions cannot be determined directly from the data.

This paper presents an analysis on the performance of the prewhitening signal covariance estimation under two kinds of non-ideal scenarios. In the first scenario, some sources exist only in the control condition and do not exist in the task condition. In the second scenario, the signal sources exist both in the control and the task conditions, and they change their intensities between the two conditions. The results from computer simulation to validate the analysis are also presented.

This paper proposes a simple statistical method for extracting target source activities from spatio-temporal source activities reconstructed from MEG measurements. The method requires measurements in a control condition, which contains only non-target source activities. The method derives, at each pixel location, an empirical probability distribution of the non-target source activity using the time course reconstruction obtained from the control period. The statistical threshold that can extract the target source activities is derived from the empirical distributions obtained from all pixel locations. Here, the multiple comparison problem is addressed with a two-step procedure involving standardizing these empirical distributions and deriving an empirical distribution of the maximum pseudo T value at each pixel location. The results of applying the proposed method to auditory-evoked measurements are presented to demonstrate the method's effectiveness.

This paper discusses the location bias and the spatial resolution in the reconstruction of a single dipole source by various spatial filtering techniques used for neuromagnetic imaging. We first analyze the location bias for several representative adaptive and non-adaptive spatial filters using their resolution kernels. This analysis theoretically validates previously reported empirical findings that standardized low-resolution electromagnetic tomography (sLORETA) has no location bias. We also find that the minimum-variance spatial filter does exhibit bias in the reconstructed location of a single source, but that this bias is eliminated by using the normalized lead field. We then focus on the comparison of sLORETA and the lead-field normalized minimum-variance spatial filter, and analyze the effect of noise on source location bias. We find that the signal-to-noise ratio (SNR) in the measurements determines whether the sLORETA reconstruction has source location bias, while the lead-field normalized minimum-variance spatial filter has no location bias even in the presence of noise. Finally, we compare the spatial resolution for sLORETA and the minimum-variance filter, and show that the minimum-variance filter attains much higher resolution than sLORETA does. The results of these analyses are validated by numerical experiments as well as by reconstructions based on two sets of evoked magnetic responses.

C OMMENTARY Cognitive Training in Schizophrenia: Golden Age or Wild West? Sophia Vinogradov, Melissa Fisher, and Srikantan Nagarajan ix years ago, at the first Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) meeting, a neuroscientist questioned whether attention dysfunction was malleable in schizophrenia, despite a recent report that patients were 5 times more likely to work when cognitive remediation was combined with supported employ- ment (1). The idea that impaired neural systems could demon- strate learning-induced plasticity was not part of the biological research lexicon at that point in time. Experimental neuroscien- tists were rightfully skeptical of a broad array of cognitive remediation interventions that were often studied under non- blinded and variously controlled conditions. Perplexing, also, was the homogeneity of effect sizes, despite widely varying treatment approaches, outcome measures, and length of intervention. S trained coach for 40 sessions of pencil-and-paper exercises in cognitive flexibility, working memory, and planning. After CRT, they demonstrated a reduction in overactivation of the central executive neural network independent component (CEN) as well as increased white matter integrity in the genu of the corpus callosum, compared with patients who met with a therapist for 40 sessions to discuss how to cope with symptoms and identify warning signs of relapse. Penade´s et al. (3) have driven the final nail into the coffin on the idea that the brain in schizophrenia is not malleable! The question is malleable in response to what? Which neurobehavioral mechanisms are driving that malleability? And what exactly does it represent in terms of both short-term and long-term significance? Golden Age or Wild West? Malleable in Response to What? Six years later, the field is at a tipping point. The systems neuroscience of learning-induced plasticity is a mature and sophisticated area of inquiry. Concomitantly, trials of cognitive enhancing medications in schizophrenia have been disappoint- ing, and investigators increasingly recognize that medications will need to be combined with cognitive enrichment strategies to drive meaningful clinical improvement. Studies of computerized cognitive training in schizophrenia show consistent patterns of increased prefrontal cortical activation as compared with various control conditions; the increased activation correlates with improvements in the trained cognitive domain, and in one instance, with transfer to an untrained meta-cognitive task and with better functioning at 6-month follow-up (2). Why, then, is the story not over? Why, when it comes to understanding how to optimize functionally meaningful and enduring cognitive improvement in schizophrenia, does it seem as if we have entered the Wild West rather than the Golden Age? How does one make sense of the plethora of computerized and noncomputerized behavioral training approaches described in the literature? Do we have any evidence whatsoever on the critical neural mechanisms that drive functionally meaningful change? Not surprisingly, the answers to these questions are complex, as highlighted by the report of Penade´s et al. (3) in this issue. Put simply, the field is poised between two opposing perspectives: one that more broadly focuses on treatment development versus a targeted focus on plasticity in defined neural systems (Table). In the study by Penade´s et al. (3), which belongs to the broad cognitive remediation legacy, 15 individuals with schizophrenia engaged in cognitive remediation therapy (CRT), meeting with a We do not know from the report by Penade´s et al. (3) whether the participants were blind to group allocation. We do not know whether the therapists in the control condition had the same training/enthusiasm/expectations for a positive outcome as did the CRT coaches, and we do not know whether the assessment personnel were blind to group assignment. Not only are all three factors a potential source of bias in the outcome data, they may actually represent critical mechanisms of neurobehavioral change. Almost half a century ago, Wagner (4) investigated brief attention and abstraction training in schizophrenia and found that the experimenter was a positive social reinforcer who clearly “enhanced the motivational condition of the subjects” and contributed to their improved cognitive performance. More recent research has established unequivocally that motivational factors and beliefs substantially affect people’s ability to recruit cognitive and neural resources to sustain learning over time (5). Thus, in the study by Penade´s et al. (3), as in many previous published reports, we cannot separate out the effects of nonspecific cognitive enhancing factors, such as therapist enthu- siasm, participant motivation and expectations, and general cognitive and socioaffective stimulation, from the effects of a specific neurobehavioral training strategy in a defined neural system (in this case, strategy coaching for executive functions and working memory). Such nonspecific cognitive enhancement is by no means a bad thing, and in clinical settings one would want to harness it as intentionally as possible, but in an experimental design, it confounds our ability to isolate and identify the precise mechanisms that drive critical responses in neural systems of interest. In this light, it is sobering to consider the highly rigorous double-blind study by Dickinson et al. (6) comparing a cognitive remediation program for schizophrenia based on problem-solving educational software plus therapist coaching versus game-based software plus coaching. This study tightly controlled for the effects of nonspecific cognitive enhan- cing factors such as participant expectation and therapist motivation and contact and found no significant group differences on neurocognitive or functional outcome measures, From the Departments of Psychiatry, (SV, MF) and Radiology and Biomedical Imaging (SN), University of California, San Francisco, San Francisco, California, and San Francisco Department of Veterans Affairs Medical Center (SV, MF), San Francisco, California. Address correspondence to Sophia Vinogradov, M.D., 116A–SFDVAMC, 4150 Clement Street, San Francisco, CA 94121; E-mail: sophia. vinogradov@ucsf.edu. Received Mar 20, 2013; accepted Mar 21, 2013. http://dx.doi.org/10.1016/j.biopsych.2013.03.015 BIOL PSYCHIATRY 2013;73:935–937 Published by Elsevier Inc on behalf of Society of Biological Psychiatry

Focal hand dystonia is a disabling, involuntary disorder of movement that can disrupt a successful musician's career. This problem is difficult to treat, to some extent because we do not fully understand its origin. Somatosensory degradation has been proposed as one etiology. The purpose of this case study was to compare the differences in the somatosensory hand representation of two female flutists, one with focal dystonia of the left hand (digits 4 and 5) and one a healthy subject (the control). Noninvasive magnetic source imaging was performed on both subjects. The somatosensory evoked potentials of controlled taps to the fingers were measured with a 37-channel biomagnetometer and reported in terms of the neuronal organization, latency, amplitude, density, location, and spread of the digits on each axis (x, y, and z). The somatosensory representation of the involved hand of the flutist with dystonia differed from that of the healthy flutist. The magnetic fields evoked from the primary somatosensory cortex had a disorganized pattern of firing, with a short latency and excessive amplitude in the involved digits of the affected hand, as well as inconsistency (decreased density). In addition, the patterns of firing were different in terms of the location of the digits on the x, y, and z axes and sequential organization of the digits. This study confirms that somatosensory evoked magnetic fields can be used to describe the representation of the hand on the somatosensory cortex in area 3b. Degradation in the hand representation of the flutist with focal hand dystonia was evident, compared with the hand representation of the healthy flutist. It is not clear whether the sensory degradation was the cause or the consequence of the dystonia. The questions are whether re-differentiation of the representation could be achieved with aggressive sensory retraining and whether improvement in structure would be correlated with improvement in function.