Multielectrode voltage data are usually recorded against a common reference. Such data are frequently used without further treatment to assess patterns of functional connectivity between neuronal populations and between brain areas. It is important to note from the outset that such an approach is valid only when the reference electrode is nearly electrically silent. In practice, however, the reference electrode is generally not electrically silent, thereby adding a common signal to the recorded data. Volume conduction further complicates the problem. In this study we demonstrate the adverse effects of common signals on the estimation of Granger causality, which is a statistical measure used to infer synaptic transmission and information flow in neural circuits from multielectrode data. We further test the hypothesis that the problem can be overcome by utilizing bipolar derivations where the difference between two nearby electrodes is taken and treated as a representation of local neural activity. Simulated data generated by a neuronal network model where the connectivity pattern is known were considered first. This was followed by analyzing data from three experimental preparations where a priori predictions regarding the patterns of causal interactions can be made: (1) laminar recordings from the hippocampus of an anesthetized rat during theta rhythm, (2) laminar recordings from V4 of an awake-behaving macaque monkey during alpha rhythm, and (3) ECoG recordings from electrode arrays implanted in the middle temporal lobe and prefrontal cortex of an epilepsy patient during fixation. For both simulation and experimental analysis the results show that bipolar derivations yield the expected connectivity patterns whereas the untreated data (referred to as unipolar signals) do not. In addition, current source density signals, where applicable, yield results that are close to the expected connectivity patterns, whereas the commonly practiced average re-reference method leads to erroneous results.

Prolonged continuous performance of a cognitively demanding task induces cognitive fatigue and is associated with a time-related deterioration of objective performance, the degree of which is referred to cognitive fatigability. Although the neural underpinnings of cognitive fatigue are poorly understood, prior studies report changes in neural activity consistent with deterioration of task-related networks over time. While compensatory brain activity is reported to maintain motor task performance in the face of motor fatigue and cognitive performance in the face of other stressors (e.g., aging) and structural changes, there are no studies to date demonstrating compensatory activity for cognitive fatigue. High-density electroencephalography was recorded from human subjects during a 160 min continuous performance of a cognitive control task. While most time-varying neural activity showed a linear decline over time, we identified an evoked potential over the anterior frontal region which demonstrated an inverted U-shaped time-on-task profile. This evoked brain activity peaked between 60 and 100 min into the task and was positively associated with better behavioral performance only during this interval. Following the peak and during subsequent decline of this anterior frontal activity, the rate of performance decline also accelerated. These findings demonstrate that this anterior frontal brain activity, which is not part of the primary task-related activity at baseline, is recruited to compensate for fatigue-induced impairments in the primary task-related network, and that this compensation terminates as cognitive fatigue further progresses. These findings may be relevant to understanding individual differences in cognitive fatigability and developing interventions for clinical conditions afflicted by fatigue.

Repetition priming is a core feature of memory processing whose anatomical correlates remain poorly understood. In this study, we use advanced multimodal imaging (functional magnetic resonance imaging (fMRI) and magnetoencephalography; MEG) to investigate the spatiotemporal profile of repetition priming. We use intracranial electroencephalography (iEEG) to validate our fMRI/MEG measurements. Twelve controls completed a semantic judgment task with fMRI and MEG that included words presented once (new, 'N') and words that repeated (old, 'O'). Six patients with epilepsy completed the same task during iEEG recordings. Blood-oxygen level dependent (BOLD) responses for N vs O words were examined across the cortical surface and within regions of interest. MEG waveforms for N vs O words were estimated using a noise-normalized minimum norm solution, and used to interpret the timecourse of fMRI. Spatial concordance was observed between fMRI and MEG repetition effects from 350-450ms within bilateral occipitotemporal and medial temporal, left prefrontal, and left posterior temporal cortex. Additionally, MEG revealed widespread sources within left temporoparietal regions, whereas fMRI revealed bilateral reductions in occipitotemporal and left superior frontal, and increases in inferior parietal, precuneus, and dorsolateral prefrontal activity. BOLD suppression in left posterior temporal, left inferior prefrontal, and right occipitotemporal cortex correlated with MEG repetition-related reductions. IEEG responses from all three regions supported the timecourse of MEG and localization of fMRI. Furthermore, iEEG decreases to repeated words were associated with decreased gamma power in several regions, providing evidence that Address for correspondence: Carrie R. McDonald, Multimodal Imaging Laboratory, 8950 Villa La Jolla Drive, Suite C101, La Jolla, CA 92037; ph: 858-534-2678; camcdonald@ucsd.edu. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. NIH Public Access Author ManuscriptNeuroimage. Author manuscript; available in PMC 2011 November 1.

Background DYT1 dystonia is an autosomal dominant movement disorder characterized by abnormal, often repetitive, movements, and postures. Its hallmark feature is sustained or intermittent contractions of muscles involving co-contractions of antagonist muscle pairs. The symptoms are relieved with the anticholinergic drug trihexyphenidyl. The primary mutation is a trinucleotide deletion (ΔGAG) in DYT1/TOR1A, which codes for torsinA. Previous studies showed that (1) heterozygous Dyt1 ΔGAG knock-in mice, which have an analogous mutation in the endogenous gene, exhibit motor deficits and altered corticostriatal synaptic plasticity in the brain, and (2) these deficits can be rescued by trihexyphenidyl. However, brain imaging studies suggest that the Dyt1 knock-in mouse models non-manifesting mutation carriers of DYT1 dystonia. Objectives To examine the hallmark features of DYT1 dystonia in the Dyt1 knock-in mice by analyzing muscular activities. Methods Wireless telemetry devices with biopotential channels were implanted to the bicep and the rectus femori muscles in the Dyt1 knock-in mice, and the muscular activities were recorded before and after trihexyphenidyl administration. Results (1) Consistent with DYT1 dystonia patients, the Dyt1 knock-in mice showed sustained contractions and co-contractions of the antagonistic bicep femoris and rectus femoris. (2) The abnormal muscle contractions were normalized by trihexyphenidyl. Conclusions The results suggest that the motor deficits in the Dyt1 knock-in mice are likely produced by the abnormal muscle contractions and the Dyt1 knock-in mice can potentially be used as a manifesting disease model to study the pathophysiology and to develop novel therapeutics.

Summary Purpose:  To examine distributed patterns of language processing in healthy controls and patients with epilepsy using magnetoencephalography (MEG), and to evaluate the concordance between laterality of distributed MEG sources and language laterality as determined by the intracarotid amobarbital procedure (IAP). Methods:  MEG was performed in 10 healthy controls using an anatomically constrained, noise‐normalized distributed source solution (dynamic statistical parametric map, dSPM). Distributed source modeling of language was then applied to eight patients with intractable epilepsy. Average source strengths within temporoparietal and frontal lobe regions of interest (ROIs) were calculated, and the laterality of activity within ROIs during discrete time windows was compared to results from the IAP. Results:  In healthy controls, dSPM revealed activity in visual cortex bilaterally from ∼80 to 120 ms in response to novel words and sensory control stimuli (i.e., false fonts). Activity then spread to fusiform cortex ∼160–200 ms, and was dominated by left hemisphere activity in response to novel words. From ∼240 to 450 ms, novel words produced activity that was left‐lateralized in frontal and temporal lobe regions, including anterior and inferior temporal, temporal pole, and pars opercularis, as well as bilaterally in posterior superior temporal cortex. Analysis of patient data with dSPM demonstrated that from 350 to 450 ms, laterality of temporoparietal sources agreed with the IAP 75% of the time, whereas laterality of frontal MEG sources agreed with the IAP in all eight patients. Discussion:  Our results reveal that dSPM can unveil the timing and spatial extent of language processes in patients with epilepsy and may enhance knowledge of language lateralization and localization for use in preoperative planning.