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Contradictory reports regarding the detection of neuronal currents have left the feasibility of neuronal current MRI (ncMRI) an open question. Most previous ncMRI studies in human subjects are suspect due to their inability to separate or eliminate hemodynamic effects. In this study, we used a bloodless turtle brain preparation that eliminates hemodynamic effects, to explore the feasibility of detecting visually-evoked ncMRI signals at 9.4 T. Intact turtle brains, with eyes attached, were dissected from the cranium and placed in artificial cerebral spinal fluid. Light flashes were delivered to the eyes to evoke neuronal activity. Local field potential (LFP) and MRI signals were measured in an interleaved fashion. Robust visually-evoked LFP signals were observed in turtle brains, but no significant signal changes synchronized with neuronal currents were found in the ncMRI images. In this study, detection thresholds of 0.1% and 0.1 degrees were set for MRI magnitude and phase signal changes, respectively. The absence of significant signal changes in the MRI images suggests that visually-evoked ncMRI signals in the turtle brain are below these detectable levels.

Electrophysiological (EEG/MEG) imaging challenges statistics by providing two views of the same underlying spatio-temporal brain activity: a topographic view (EEG/MEG) and tomographic view (EEG/MEG source reconstructions). It is a common practice that statistical parametric mapping (SPM) for these two situations is developed separately. In particular, assessing statistical significance of functional connectivity is a major challenge in these types of studies. This work introduces statistical tests for assessing simultaneously the significance of spatio-temporal correlation structure between ERP/ERF components as well as that of their generating sources. We introduce a greatest root statistic as the multivariate test statistic for detecting functional connectivity between two sets of EEG/MEG measurements at a given time instant. We use some new results in random field theory to solve the multiple comparisons problem resulting from the correlated test statistics at each time instant. In general, our approach using the union-intersection (UI) principle provides a framework for hypothesis testing about any linear combination of sensor data, which allows the analysis of the correlation structure of both topographic and tomographic views. The performance of the proposed method is illustrated with real ERP data obtained from a face recognition experiment., ((c) 2009 Wiley-Liss, Inc.)

Temporal lobe epilepsy (TLE) is considered primarily a limbic disorder. Our purpose was to map limbic network organization in TLE and to statistically relate it to neocortical atrophy. We performed MRI-based cortical thickness analysis in 110 TLE patients (including 68 patients with hippocampal atrophy and 42 patients with normal hippocampal volume) and 46 healthy controls. Limbic connectivity was statistically inferred by correlating mean thickness of the entorhinal cortex (EC) with thickness at each vertex across the entire neocortex. The EC was chosen as seed region since it is the link between the neocortex and the hippocampal formation. Patients showed cortical thinning mainly in temporal and fronto-central neocortices, with a prevalence of atrophy in up to 35%. In controls, EC networks corresponded closely to known anatomical connections. In TLE the pattern of correlations was similar to controls, suggesting that pathological processes in the EC affect the same networks that co-vary with the EC in the healthy brain. Nevertheless, we found decreases in correlations mainly in the temporal lobe and increases mainly in orbitofrontal cortices. Although our analysis indicated alterations in the temporo-limbic network in TLE, there was no association between mesiotemporal connectivity and atrophy across the entire cortical surface. This divergence underlines the complexity of the pathophysiological mechanisms leading to neocortical atrophy in TLE.

We introduce MACACC-Mapping Anatomical Correlations Across Cerebral Cortex-to study correlated changes within and across different cortical networks. The principal topic of investigation is whether the thickness of one area of the cortex changes in a statistically correlated fashion with changes in thickness of other cortical regions. We further extend these methods by introducing techniques to test whether different population groupings exhibit significantly varying MACACC patterns. The methods are described in detail and applied to a normal childhood development population (n = 292), and show that association cortices have the highest correlation strengths. Taking Brodmann Area (BA) 44 as a seed region revealed MACACC patterns strikingly similar to tractography maps obtained from diffusion tensor imaging. Furthermore, the MACACC map of BA 44 changed with age, older subjects featuring tighter correlations with BA 44 in the anterior portions of the superior temporal gyri. Lastly, IQ-dependent MACACC differences were investigated, revealing steeper correlations between BA 44 and multiple frontal and parietal regions for the higher IQ group, most significantly (t = 4.0) in the anterior cingulate.

We compare two common methods for detecting functional connectivity: thresholding correlations and singular value decomposition (SVD). We find that thresholding correlations are better at detecting focal regions of correlated voxels, whereas SVD is better at detecting extensive regions of correlated voxels. We apply these results to resting state networks in an fMRI dataset to look for connectivity in cortical thickness.

Because of their increased sensitivity to spatially extended signals, cluster-size tests are widely used to detect changes and activations in brain images. However, when images are nonstationary, the cluster-size distribution varies depending on local smoothness. Clusters tend to be large in smooth regions, resulting in increased false positives, while in rough regions, clusters tend to be small, resulting in decreased sensitivity. Worsley et al. proposed a random field theory (RFT) method that adjusts cluster sizes according to local roughness of images [Worsley, K.J., 2002. Nonstationary FWHM and its effect on statistical inference of fMRI data. Presented at the 8th International Conference on Functional Mapping of the Human Brain, June 2-6, 2002, Sendai, Japan. Available on CD-ROM in NeuroImage 16 (2) 779-780; Hum. Brain Mapp. 8 (1999) 98]. In this paper, we implement this method in a permutation test framework, which requires very few assumptions, is known to be exact [J. Cereb. Blood Flow Metab. 16 (1996) 7] and is robust [NeuroImage 20 (2003) 2343]. We compared our method to stationary permutation, stationary RFT, and nonstationary RFT methods. Using simulated data, we found that our permutation test performs well under any setting examined, whereas the nonstationary RFT test performs well only for smooth images under high df. We also found that the stationary RFT test becomes anticonservative under nonstationarity, while both nonstationary RFT and permutation tests remain valid under nonstationarity. On a real PET data set we found that, though the nonstationary tests have reduced sensitivity due to smoothness estimation variability, these tests have better sensitivity for clusters in rough regions compared to stationary cluster-size tests. We include a detailed and consolidated description of Worsley nonstationary RFT cluster-size test.

In multiple sclerosis (MS), the correlation between disability and the volume of white matter lesions on magnetic resonance imaging (MRI) is usually weak. This may be because lesion location also influences the extent and type of functional disability. We applied an automatic lesion-detection algorithm to 452 MRI scans of patients with relapsing-remitting MS to identify the regions preferentially responsible for different types of clinical deficits. Statistical parametric maps were generated by performing voxel-wise linear regressions between lesion probability and different clinical disability scores. There was a clear distinction between lesion locations causing physical and cognitive disability. Lesion likelihood correlated with the Expanded Disability Status Scale (EDSS) in the left internal capsule and in periventricular white matter mostly in the left hemisphere. Pyramidal deficits correlated with only one area in the left internal capsule that was also present in the EDSS correlation. Cognitive dysfunction correlated with lesion location at the grey-white junction of associative, limbic, and prefrontal cortex. Coordination impairment correlated with areas in interhemispheric and pyramidal periventricular white matter tracts, and in the inferior and superior longitudinal fascicles. Bowel and bladder scores correlated with lesions in the medial frontal lobes, cerebellum, insula, dorsal midbrain, and pons, areas known to be involved in the control of micturition. This study demonstrates for the first time a relationship between the site of lesions and the type of disability in large scale MRI data set in MS.

Abstract: Epileptic activity underlying idiopathic generalized epilepsy (IGE) is related to abnormal thalamo–cortical interactions. Our purpose was to map in vivo the organization of the thalamo–cortical network in IGE. We measured cortical thickness and thalamic volumes on MRI in 23 IGE patients with generalized tonic–clonic seizures only and 46 healthy controls. Significant correlations between thalamic volumes and cortical thickness were interpreted as thalamo–cortical network connections. In controls, thickness of frontal, limbic, and occipital regions was positively correlated with the thalamic volumes, corresponding to known anatomical connections from sacrificial tracer studies in primates and human in vivo DTI data. In patients, thalamo–cortical network correlations increased in fronto-central and parietal regions, but decreased in limbic areas. Group analysis revealed that, compared to controls, IGE patients had bilateral thalamic atrophy and widespread cortical thinning that was most prominent in fronto-central areas, with a prevalence of up to 40%. Duration of epilepsy affected negatively thalamic volumes and thickness of fronto-central and limbic cortices. These effects were significantly different from aging in controls. Patients with poorly controlled seizures showed an even faster progression in these neocortical regions. Fronto-centro-parietal atrophy in IGE is likely the effect of generalized seizure activity inducing thalamo–cortical network remodeling. On the other hand, limbic abnormalities may take place secondary to thalamic disconnection. [Copyright &y& Elsevier]