Sustaining sleep spindles through enhanced SK2-channel activity consolidates sleep and elevates arousal threshold.

Sleep spindles are synchronized 11-15 Hz electroencephalographic (EEG) oscillations predominant during nonrapid-eye-movement sleep (NREMS). Rhythmic bursting in the reticular thalamic nucleus (nRt), arising from interplay between Ca(v)3.3-type Ca(2+) channels and Ca(2+)-dependent small-conductance-type 2 (SK2) K(+) channels, underlies spindle generation. Correlative evidence indicates that spindles contribute to memory consolidation and protection against environmental noise in human NREMS. Here, we describe a molecular mechanism through which spindle power is selectively extended and we probed the actions of intensified spindling in the naturally sleeping mouse. Using electrophysiological recordings in acute brain slices from SK2 channel-overexpressing (SK2-OE) mice, we found that nRt bursting was potentiated and thalamic circuit oscillations were prolonged. Moreover, nRt cells showed greater resilience to transit from burst to tonic discharge in response to gradual depolarization, mimicking transitions out of NREMS. Compared with wild-type littermates, chronic EEG recordings of SK2-OE mice contained less fragmented NREMS, while the NREMS EEG power spectrum was conserved. Furthermore, EEG spindle activity was prolonged at NREMS exit. Finally, when exposed to white noise, SK2-OE mice needed stronger stimuli to arouse. Increased nRt bursting thus strengthens spindles and improves sleep quality through mechanisms independent of EEG slow waves (<4 Hz), suggesting SK2 signaling as a new potential therapeutic target for sleep disorders and for neuropsychiatric diseases accompanied by weakened sleep spindles.

Preperceptual and stimulus-selective enhancement of low-level human visual cortex excitability by sounds.

Evidence of multisensory interactions within low-level cortices and at early post-stimulus latencies has prompted a paradigm shift in conceptualizations of sensory organization. However, the mechanisms of these interactions and their link to behavior remain largely unknown. One behaviorally salient stimulus is a rapidly approaching (looming) object, which can indicate potential threats. Based on findings from humans and nonhuman primates suggesting there to be selective multisensory (auditory-visual) integration of looming signals, we tested whether looming sounds would selectively modulate the excitability of visual cortex. We combined transcranial magnetic stimulation (TMS) over the occipital pole and psychophysics for "neurometric" and psychometric assays of changes in low-level visual cortex excitability (i.e., phosphene induction) and perception, respectively. Across three experiments we show that structured looming sounds considerably enhance visual cortex excitability relative to other sound categories and white-noise controls. The time course of this effect showed that modulation of visual cortex excitability started to differ between looming and stationary sounds for sound portions of very short duration (80 ms) that were significantly below (by 35 ms) perceptual discrimination threshold. Visual perceptions are thus rapidly and efficiently boosted by sounds through early, preperceptual and stimulus-selective modulation of neuronal excitability within low-level visual cortex.

Learning-induced plasticity in auditory spatial representations revealed by electrical neuroimaging.

Auditory spatial representations are likely encoded at a population level within human auditory cortices. We investigated learning-induced plasticity of spatial discrimination in healthy subjects using auditory-evoked potentials (AEPs) and electrical neuroimaging analyses. Stimuli were 100 ms white-noise bursts lateralized with varying interaural time differences. In three experiments, plasticity was induced with 40 min of discrimination training. During training, accuracy significantly improved from near-chance levels to approximately 75%. Before and after training, AEPs were recorded to stimuli presented passively with a more medial sound lateralization outnumbering a more lateral one (7:1). In experiment 1, the same lateralizations were used for training and AEP sessions. Significant AEP modulations to the different lateralizations were evident only after training, indicative of a learning-induced mismatch negativity (MMN). More precisely, this MMN at 195-250 ms after stimulus onset followed from differences in the AEP topography to each stimulus position, indicative of changes in the underlying brain network. In experiment 2, mirror-symmetric locations were used for training and AEP sessions; no training-related AEP modulations or MMN were observed. In experiment 3, the discrimination of trained plus equidistant untrained separations was tested psychophysically before and 0, 6, 24, and 48 h after training. Learning-induced plasticity lasted <6 h, did not generalize to untrained lateralizations, and was not the simple result of strengthening the representation of the trained lateralizations. Thus, learning-induced plasticity of auditory spatial discrimination relies on spatial comparisons, rather than a spatial anchor or a general comparator. Furthermore, cortical auditory representations of space are dynamic and subject to rapid reorganization.

Individual Classification of Mild Cognitive Impairment Subtypes by Support Vector Machine Analysis of White Matter DTI.

BACKGROUND AND PURPOSE: MCI was recently subdivided into sd-aMCI, sd-fMCI, and md-aMCI. The current investigation aimed to discriminate between MCI subtypes by using DTI. MATERIALS AND METHODS: Sixty-six prospective participants were included: 18 with sd-aMCI, 13 with sd-fMCI, and 35 with md-aMCI. Statistics included group comparisons using TBSS and individual classification using SVMs. RESULTS: The group-level analysis revealed a decrease in FA in md-aMCI versus sd-aMCI in an extensive bilateral, right-dominant network, and a more pronounced reduction of FA in md-aMCI compared with sd-fMCI in right inferior fronto-occipital fasciculus and inferior longitudinal fasciculus. The comparison between sd-fMCI and sd-aMCI, as well as the analysis of the other diffusion parameters, yielded no significant group differences. The individual-level SVM analysis provided discrimination between the MCI subtypes with accuracies around 97%. The major limitation is the relatively small number of cases of MCI. CONCLUSIONS: Our data show that, at the group level, the md-aMCI subgroup has the most pronounced damage in white matter integrity. Individually, SVM analysis of white matter FA provided highly accurate classification of MCI subtypes.

Proton T1 relaxation times of metabolites in human occipital white and gray matter at 7 T.

Proton T1 relaxation times of metabolites in the human brain have not previously been published at 7 T. In this study, T1 values of CH3 and CH2 group of N-acetylaspartate and total creatine as well as nine other brain metabolites were measured in occipital white matter and gray matter at 7 T using an inversion-recovery technique combined with a newly implemented semi-adiabatic spin-echo full-intensity acquired localized spectroscopy sequence (echo time = 12 ms). The mean T1 values of metabolites in occipital white matter and gray matter ranged from 0.9 to 2.2 s. Among them, the T1 of glutathione, scyllo-inositol, taurine, phosphorylethanolamine, and N-acetylaspartylglutamate were determined for the first time in the human brain. Significant differences in T1 between white matter and gray matter were found for water (-28%), total choline (-14%), N-acetylaspartylglutamate (-29%), N-acetylaspartate (+4%), and glutamate (+8%). An increasing trend in T1 was observed when compared with previously reported values of N-acetylaspartate (CH3 ), total creatine (CH3 ), and total choline at 3 T. However, for N-acetylaspartate (CH3 ), total creatine, and total choline, no substantial differences compared to previously reported values at 9.4 T were discernible. The T1 values reported here will be useful for the quantification of metabolites and signal-to-noise optimization in human brain at 7 T. Magn Reson Med 69:931-936, 2013. © 2012 Wiley Periodicals, Inc.

Estimating the confidence level of white matter connections obtained with MRI tractography.

BACKGROUND: Since the emergence of diffusion tensor imaging, a lot of work has been done to better understand the properties of diffusion MRI tractography. However, the validation of the reconstructed fiber connections remains problematic in many respects. For example, it is difficult to assess whether a connection is the result of the diffusion coherence contrast itself or the simple result of other uncontrolled parameters like for example: noise, brain geometry and algorithmic characteristics. METHODOLOGY/PRINCIPAL FINDINGS: In this work, we propose a method to estimate the respective contributions of diffusion coherence versus other effects to a tractography result by comparing data sets with and without diffusion coherence contrast. We use this methodology to assign a confidence level to every gray matter to gray matter connection and add this new information directly in the connectivity matrix. CONCLUSIONS/SIGNIFICANCE: Our results demonstrate that whereas we can have a strong confidence in mid- and long-range connections obtained by a tractography experiment, it is difficult to distinguish between short connections traced due to diffusion coherence contrast from those produced by chance due to the other uncontrolled factors of the tractography methodology.

High b-value Diffusion-weighted Imaging: A Sensitive Method to Reveal White Matter Changes in Schizophrenia

Background: b-value is the parameter characterizing the intensity of the diffusion weighting during image acquisition. Data acquisition is usually performed with low b value (b~1000 s/mm2). Evidence shows that high b-values (b>2000 s/mm2) are more sensitive to the slow diffusion compartment (SDC) and maybe more sensitive in detecting white matter (WM) anomalies in schizophrenia.Methods: 12 male patients with schizophrenia (mean age 35 +/-3 years) and 16 healthy male controls matched for age were scanned with a low b-value (1000 s/mm2) and a high b-value (4000 s/mm2) protocol. Apparent diffusion coefficient (ADC) is a measure of the average diffusion distance of water molecules per time unit (mm2/s). ADC maps were generated for all individuals. 8 region of interests (frontal and parietal region bilaterally, centrum semi-ovale bilaterally and anterior and posterior corpus callosum) were manually traced blind to diagnosis.Results: ADC measures acquired with high b-value imaging were more sensitive in detecting differences between schizophrenia patients and healthy controls than low b-value imaging with a gain in significance by a factor of 20- 100 times despite the lower image Signal-to-noise ratio (SNR). Increased ADC was identified in patient's WM (p=0.00015) with major contributions from left and right centrum semi-ovale and to a lesser extent right parietal region.Conclusions: Our results may be related to the sensitivity of high b-value imaging to the SDC believed to reflect mainly the intra-axonal and myelin bound water pool. High b-value imaging might be more sensitive and specific to WM anomalies in schizophrenia than low b-value imaging