Mind over chatter: plastic up-regulation of the fMRI alertness network by EEG neurofeedback

EEG neurofeedback (NFB) is a brain-computer interface (BCI) approach used to shape brain oscillations by means of real-time feedback from the electroencephalogram (EEG), which is known to reflect neural activity across cortical networks. Although NFB is being evaluated as a novel tool for treating brain disorders, evidence is scarce on the mechanism of its impact on brain function. In this study with 34 healthy participants, we examined whether, during the performance of an attentional auditory oddball task, the functional connectivity strength of distinct fMRI networks would be plastically altered after a 30-min NFB session of alpha-band reduction (n=17) versus a sham-feedback condition (n=17). Our results reveal that compared to sham, NFB induced a specific increase of functional connectivity within the alertness/salience network (dorsal anterior and mid cingulate), which was detectable 30 minutes after termination of training. Crucially, these effects were significantly correlated with reduced mind-wandering 'on-task' and were coupled to NFB-mediated resting state reductions in the alpha-band (8-12 Hz). No such relationships were evident for the sham condition. Although group default-mode network (DMN) connectivity was not significantly altered following NFB, we observed a positive association between modulations of resting alpha amplitude and precuneal connectivity, both correlating positively with frequency of mind-wandering. Our findings demonstrate a temporally direct, plastic impact of NFB on large-scale brain functional networks, and provide promising neurobehavioral evidence supporting its use as a noninvasive tool to modulate brain function in health and disease

Very early responses to colour stimuli detected in prestriate visual cortex by magnetoencephalography (MEG)

Our previous studies with the visual motion and form systems show that visual stimuli belonging to these categories trigger much earlier latency responses from the visual cortex than previously supposed and that the source of the earliest signals can be located in either the prestriate cortex or in both the striate (V1) and prestriate cortex. This is consistent with the known anatomical connections since, in addition to the classical retino-geniculo-striate cortex input to the prestriate visual areas, there are direct anatomical inputs from both the lateral geniculate nucleus and the pulvinar that reach the prestriate visual cortex without passing through striate cortex. In pursuing our studies, we thought it especially interesting to study another cardinal visual attribute, namely colour, to learn whether colour stimuli also provoke very early responses, at less than 50 ms from visual cortex. To address the question, we asked participants to view stimuli that changed in colour and used magneto-encephalography to detect very early responses (< 50 ms) in the occipital visual cortex. Our results show that coloured stimuli also provoke an early cortical response (M30), with an average peak time at 31.7 ms, thus bringing the colour system into line with the visual motion and form systems. We conclude that colour signals reach visual cortex, including prestriate visual cortex, earlier than previously supposed

Gate-level timing analysis and waveform evaluation

Static timing analysis (STA) is an integral part of modern VLSI chip design. Table lookup based methods are widely used in current industry due to its fast runtime and mature algorithms. Conventional STA algorithms based on table-lookup methods are developed under many assumptions in timing analysis; however, most of those assumptions, such as that input signals and output signals can be accurately modeled as ramp waveforms, are no longer satisfactory to meet the increasing demand of accuracy for new technologies. In this dissertation, we discuss several crucial issues that conventional STA has not taken into consideration, and propose new methods to handle these issues and show that new methods produce accurate results. In logic circuits, gates may have multiple inputs and signals can arrive at these inputs at different times and with different waveforms. Different arrival times and waveforms of signals can cause very different responses. However, multiple-input transition effects are totally overlooked by current STA tools. Using a conventional single-input transition model when multiple-input transition happens can cause significant estimation errors in timing analysis. Previous works on this issue focus on developing a complicated gate model to simulate the behavior of logic gates. These methods have high computational cost and have to make significant changes to the prevailing STA tools, and are thus not feasible in practice. This dissertation proposes a simplified gate model, uses transistor connection structures to capture the behavior of multiple-input transitions and requires no change to the current STA tools. Another issue with table lookup based methods is that the load of each gate in technology libraries is modeled as a single lumped capacitor. But in the real circuit, the Abstract 2 gate connects to its subsequent gates via metal wires. As the feature size of integrated circuit scales down, the interconnection cannot be seen as a simple capacitor since the resistive shielding effect will largely affect the equivalent capacitance seen from the gate. As the interconnection has numerous structures, tabulating the timing data for various interconnection structures is not feasible. In this dissertation, by using the concept of equivalent admittance, we reduce an arbitrary interconnection structure into an equivalent π-model RC circuit. Many previous works have mapped the π-model to an effective capacitor, which makes the table lookup based methods useful again. However, a capacitor cannot be equivalent to a π-model circuit, and will thus result in significant inaccuracy in waveform evaluation. In order to obtain an accurate waveform at gate output, a piecewise waveform evaluation method is proposed in this dissertation. Each part of the piecewise waveform is evaluated according to the gate characteristic and load structures. Another contribution of this dissertation research is a proposed equivalent waveform search method. The signal waveforms can be very complicated in the real circuits because of noises, race hazards, etc. The conventional STA only uses one attribute (i.e., transition time) to describe the waveform shape which can cause significant estimation errors. Our approach is to develop heuristic search functions to find equivalent ramps to approximate input waveforms. Here the transition time of a final ramp can be completely different from that of the original waveform, but we can get higher accuracy on output arrival time and transition time. All of the methods mentioned in this dissertation require no changes to the prevailing STA tools, and have been verified across different process technologies

Diffusion-Weighted Imaging: Recent Advances and Applications

Quantitative diffusion imaging techniques enable the characterization of tissue microstructural properties of the human brain “in vivo”, and are widely used in neuroscientific and clinical contexts. In this review, we present the basic physical principles behind diffusion imaging and provide an overview of the current diffusion techniques, including standard and advanced techniques as well as their main clinical applications. Standard diffusion tensor imaging (DTI) offers sensitivity to changes in microstructure due to diseases and enables the characterization of single fiber distributions within a voxel as well as diffusion anisotropy. Nonetheless, its inability to represent complex intravoxel fiber topologies and the limited biological specificity of its metrics motivated the development of several advanced diffusion MRI techniques. For example, high-angular resolution diffusion imaging (HARDI) techniques enabled the characterization of fiber crossing areas and other complex fiber topologies in a single voxel and supported the development of higher-order signal representations aiming to decompose the diffusion MRI signal into distinct microstructure compartments. Biophysical models, often known by their acronym (e.g., CHARMED, WMTI, NODDI, DBSI, DIAMOND) contributed to capture the diffusion properties from each of such tissue compartments, enabling the computation of voxel-wise maps of axonal density and/or morphology that hold promise as clinically viable biomarkers in several neurological and neuroscientific applications; for example, to quantify tissue alterations due to disease or healthy processes. Current challenges and limitations of state-of-the-art models are discussed, including validation efforts. Finally, novel diffusion encoding approaches (e.g., b-tensor or double diffusion encoding) may increase the biological specificity of diffusion metrics towards intra-voxel diffusion heterogeneity in clinical settings, holding promise in neurological applications

Integration of identity and emotion information in faces: fMRI evidence.

Separate neural systems have been implicated in the recognition of facial identity and emotional expression. A growing number of studies now provide evidence against this modular view by demonstrating that integration of identity and emotion information enhances face processing. Yet, the neural mechanisms that shape this integration remain largely unknown. We hypothesize that the presence of both personal and emotional expression target information triggers changes in functional connectivity between frontal and extrastriate areas in the brain. We report and discuss three important findings. First, the presence of target identity and emotional expression in the same face was associated with super capacity and violations of the independent processing of identity and expression cues. Second, activity in the orbitofrontal cortex (OFC) was associated with the presence of redundant targets and changes in functional connectivity between a particular region of the right OFC (BA11/47) and bilateral visual brain regions (the inferior occipital gyrus (IOG)). Third, these changes in connectivity showed a strong link to behavioural measures of capacity processing. We suggest that the changes in functional connectivity between the right OFC and IOG reduce variability of BOLD responses in the IOG, enhancing integration of identity and emotional expression cues in faces

Detecting and representing predictable structure during auditory scene analysis

We use psychophysics and MEG to test how sensitivity to input statistics facilitates auditory-scene-analysis (ASA). Human subjects listened to ‘scenes’ comprised of concurrent tonepip streams (sources). On occasional trials a new source appeared partway. Listeners were more accurate and quicker to detect source appearance in scenes comprised of temporally-regular (REG), rather than random (RAND), sources. MEG in passive listeners and those actively detecting appearance events revealed increased sustained activity in auditory and parietal cortex in REG relative to RAND scenes, emerging ~400 ms of scene-onset. Over and above this, appearance in REG scenes was associated with increased responses relative to RAND scenes. The effect of temporal structure on appearance-evoked responses was delayed when listeners were focused on the scenes relative to when listening passively, consistent with the notion that attention reduces ‘surprise’. Overall, the results implicate a mechanism that tracks predictability of multiple concurrent sources to facilitate active and passive ASA

Neurofunctional and Behavioral Correlates of Phonetic and Temporal Categorization in Musically Trained and Untrained Subjects

The perception of rapidly changing verbal and nonverbal auditory patterns is a fundamental prerequisite for speech and music processing. Previously, the left planum temporale (PT) has been consistently shown to support the discrimination of fast changing verbal and nonverbal sounds. Furthermore, it has been repeatedly shown that the functional and structural architecture of this supratemporal brain region differs as a function of musical training. In the present study, we used the functional magnetic resonance imaging technique, in a sample of professional musicians and nonmusicians, in order to examine the functional contribution of the left PT to the categorization of consonant-vowel syllables and their reduced-spectrum analogues. In line with our hypothesis, the musicians showed enhanced brain responses in the left PT and superior discrimination abilities in the reduced-spectrum condition. Moreover, we found a positive correlation between the responsiveness of the left PT and the performance in the reduced-spectrum condition across all subjects irrespective of musical expertise. These results have implications for our understanding of musical expertise in relation to segmental speech processin

Cortico-brainstem mechanisms of biased perceptual decision-making in the context of pain

Perceptual decision-making is commonly studied using stimuli with different physical properties but of comparable affective value. Here, we investigate neural processes underlying human perceptual decisions in the affectively rich domain of pain using a drift-diffusion model in combination with a probabilistic cueing paradigm. This allowed us to characterize a novel role for the dorsolateral prefrontal cortex (DLPFC), whose anticipatory responses reflecting a decision bias were dependent on the affective value of the stimulus. During intense noxious stimulation, these model-based anticipatory DLPFC responses were linked to an engagement of the periaqueductal gray (PAG), a midbrain region implicated in defensive responses including analgesia. Complementing these findings on biased decision-making, the model parameter reflecting sensory processing predicted subcortical responses (in amygdala and PAG) when expectations were violated. Our findings highlight the importance of taking a broader perspective on perceptual decisions and link decisions about pain with subcortical circuitry implicated in endogenous pain modulation

Personalizing functional Magnetic Resonance Protocols for Studying Neural Substrates of Motor Deficits in Parkinson’s Disease

Parkinson’s disease (PD) is a progressive neurodegenerative movement disorder characterized by a large number of motor and non-motor deficits, which significantly contribute to reduced quality of life. Despite the definition of the broad spectrum of clinical characteristics, mechanisms triggering illness, the nature of its progression and a character of therapeutic effects still remain unknown. The enormous advances in magnetic resonance imaging (MRI) in the last decades have significantly affected the research attempts to uncover the functional and structural abnormalities in PD and have helped to develop and monitor various treatment strategies, of which dopamine replacement strategies, mainly in form of levodopa, has been the gold standard since the late seventies and eighties. Motor, task-related functional MRI (fMRI) has been extensively used to assess the pathological state of the motor circuitry in PD. Several studies employed motor paradigms and fMRI to review the functional brain responses of participants to levodopa treatment. Interestingly, they provided conflicting results. Wide spectrum of symptoms, variability and asymmetry of the disease presentation, several treatment approaches and their divergent outcomes make PD enormously heterogeneous. In this work we hypothesized that not considering the disease heterogeneity might have been an adequate cause for the discrepant results in aforementioned studies. We show that not accounting for the disease variability might indeed compromise the results and invalidate the consequent interpretations. Accordingly, we propose and formalize a statistical approach to account for the intra and inter subject variability. This might help to minimize this bias in future motor fMRI studies revealing the functional brain dysfunction and contribute to the understanding of still unknown pathophysiological mechanisms underlying PD

Mutation-related magnetization-transfer, not axon density, drives white matter differences in premanifest Huntington disease:Evidence from in vivo ultra-strong gradient MRI

White matter (WM) alterations have been observed in Huntington disease (HD) but their role in the disease-pathophysiology remains unknown. We assessed WM changes in premanifest HD by exploiting ultra-strong-gradient magnetic resonance imaging (MRI). This allowed to separately quantify magnetization transfer ratio (MTR) and hindered and restricted diffusion-weighted signal fractions, and assess how they drove WM microstructure differences between patients and controls. We used tractometry to investigate region-specific alterations across callosal segments with well-characterized early- and late-myelinating axon populations, while brain-wise differences were explored with tract-based cluster analysis (TBCA). Behavioral measures were included to explore disease-associated brain-function relationships. We detected lower MTR in patients' callosal rostrum (tractometry: p = .03; TBCA: p = .03), but higher MTR in their splenium (tractometry: p = .02). Importantly, patients' mutation-size and MTR were positively correlated (all p-values < .01), indicating that MTR alterations may directly result from the mutation. Further, MTR was higher in younger, but lower in older patients relative to controls (p = .003), suggesting that MTR increases are detrimental later in the disease. Finally, patients showed higher restricted diffusion signal fraction (FR) from the composite hindered and restricted model of diffusion (CHARMED) in the cortico-spinal tract (p = .03), which correlated positively with MTR in the posterior callosum (p = .033), potentially reflecting compensatory mechanisms. In summary, this first comprehensive, ultra-strong gradient MRI study in HD provides novel evidence of mutation-driven MTR alterations at the premanifest disease stage which may reflect neurodevelopmental changes in iron, myelin, or a combination of these