Separable amygdala activation patterns in the evaluations of robots.

Given the increasing presence of robots in everyday environments and the significant challenge posed by social interactions with robots, it is crucial to gain a deeper understanding into the social evaluations of robots. One potentially effective approach to comprehend the fundamental processes underlying controlled and automatic evaluations of robots is to probe brain response to different perception levels of robot-related stimuli. Here, we investigate controlled and automatic evaluations of robots based on brain responses during viewing of suprathreshold (duration: 200 ms) and subthreshold (duration: 17 ms) humanoid robot stimuli. Our behavioral analysis revealed that despite participants' self-reported positive attitudes, they held negative implicit attitudes toward humanoid robots. Neuroimaging analysis indicated that subthreshold presentation of humanoid robot stimuli elicited significant activation in the left amygdala, which was associated with negative implicit attitudes. Conversely, no significant left amygdala activation was observed during suprathreshold presentation. Following successful attenuation of negative attitudes, the left amygdala response to subthreshold presentation of humanoid robot stimuli decreased, and this decrease correlated positively with the reduction in negative attitudes. These findings provide evidence for separable patterns of amygdala activation between controlled and automatic processing of robots, suggesting that controlled evaluations may influence automatic evaluations of robots.

Live Imaging of 3D Hanging Drop Arrays through Manipulation of Light-Responsive Pyroelectric Slippery Surface and Chip Adhesion.

Three-dimensional (3D) hanging drop cell culture is widely used in organoid culture because of its lack of selection pressure and rapid cell aggregation. However, current hanging drop technology has limitations, such as a dependence on complex microfluidic transport channels or specific capillary force templates for drop formation, which leads to unchangeable drop features. These methods also hinder live imaging because of space and complexity constraints. Here, we have developed a hanging drop construction method and created a flexible 3D hanging drop construction platform composed of a manipulation module and an adhesion module. Their harmonious operation allows for the easy construction of hanging drops of varying sizes, types, and patterns. Our platform produces a cell hanging drop chip with small sizes and clear fields of view, thereby making it compatible with live imaging. This platform has great potential for personalized medicine, cancer and drug discovery, tissue engineering, and stem cell research.

Effects of radiofrequency channel numbers on B1+ mapping using the Bloch-Siegert shift method.

PURPOSE: This paper aims to investigate the impact of the channel numbers on the performance of B1+ mapping, by using the Bloch-Siegert shift (BSS) method. B1+ mapping plays a crucial role in various brain imaging protocols. THEORY AND METHODS: We simulated the radiofrequency field of the human head model in six groups of multi-channel receive coil with a range of different channel numbers. MR signals were synthesized according to the standard BSS sequence, with quantified Gaussian added. Next, we combined the signals of each channel to reconstruct the B1+ map by weighted averaging and maximum likelihood estimation strategies and evaluate the bias by relative standard deviation of each coil. RESULTS: The simulation results revealed that the accuracy of B1+ maps improved with the increasing of channel numbers, meanwhile the per channel efficiency of B1+maps accuracy gradually decrease. Both trends slowed down when the channel numbers reached 12 or above. CONCLUSION: Our finding suggests that increasing the channel numbers can improve the accuracy of B1+map. However, a diminishing efficiency of per channel accuracy improvement was overserved, indicating that the relationship between quality of B1+ map and the channel numbers is nonlinear. Based on these findings, our study provides a reference for determining channel numbers to achieve a balance of coil selection and manufacturing cost. It also provides a theoretical basis for evaluating other B1+ mapping techniques.

White matter BOLD signals at 7 Tesla reveal visual field maps in optic radiation and vertical occipital fasciculus.

There is growing evidence that blood-oxygen-level-dependent (BOLD) activity in the white matter (WM) can be detected by functional magnetic resonance imaging (fMRI). However, the functional relevance and significance of WM BOLD signals remain controversial. Here we investigated whether 7T BOLD fMRI can reveal fine-scale functional organizations of a WM bundle. Population receptive field (pRF) analyses of the 7T retinotopy dataset from the Human Connectome Project revealed clear contralateral retinotopic organizations of two visual WM bundles: the optic radiation (OR) and the vertical occipital fasciculus (VOF). The retinotopic maps of OR are highly consistent with post-mortem dissections and diffusion tractographies, while the VOF maps are compatible with the dorsal and ventral visual areas connected by the WM. Similar to the grey matter (GM) visual areas, both WM bundles show over-representations of the central visual field and increasing pRF size with eccentricity. Hemodynamic response functions of visual WM were slower and wider compared with those of GM areas. These findings clearly demonstrate that WM BOLD at 7 Tesla is closely coupled with neural activity related to axons, encoding highly specific information that can be used to characterize fine-scale functional organizations of a WM bundle.

Accelerating Neurite Growth and Directing Neuronal Connections Constrained by 3D Porous Microtubes.

Investigation of neural growth and connection is crucial in the field of neural tissue engineering. Here, using a femtosecond laser direct writing (fs-DLW) technique, we propose a directionally aligned porous microtube array as a culture system for accelerating the growth of neurons and directing the connection of neurites. These microtubes exhibited an unprecedented guidance effect toward the outgrowth of primary embryonic rat hippocampal neurons, with a wrap resembling the myelin sheaths of neurons. The speed of neurite growth inside these microtubes was significantly faster than that outside these microtubes. We also achieved selective/directing connection of neural networks inside the magnetic microtubes via precise microtube delivery to a gap between two neural clusters. This work not only proposes a powerful microtube platform for accelerated growth of neurons but also offers a new idea for constructing biological neural circuits by arranging the size, location, and pattern of microtubes.

Attention module improves both performance and interpretability of four-dimensional functional magnetic resonance imaging decoding neural network.

Decoding brain cognitive states from neuroimaging signals is an important topic in neuroscience. In recent years, deep neural networks (DNNs) have been recruited for multiple brain state decoding and achieved good performance. However, the open question of how to interpret the DNN black box remains unanswered. Capitalizing on advances in machine learning, we integrated attention modules into brain decoders to facilitate an in-depth interpretation of DNN channels. A four-dimensional (4D) convolution operation was also included to extract temporo-spatial interaction within the fMRI signal. The experiments showed that the proposed model obtains a very high accuracy (97.4%) and outperforms previous researches on the seven different task benchmarks from the Human Connectome Project (HCP) dataset. The visualization analysis further illustrated the hierarchical emergence of task-specific masks with depth. Finally, the model was retrained to regress individual traits within the HCP and to classify viewing images from the BOLD5000 dataset, respectively. Transfer learning also achieves good performance. Further visualization analysis shows that, after transfer learning, low-level attention masks remained similar to the source domain, whereas high-level attention masks changed adaptively. In conclusion, the proposed 4D model with attention module performed well and facilitated interpretation of DNNs, which is helpful for subsequent research.

Functional and structural alterations in the pain-related circuit in major depressive disorder induced by electroconvulsive therapy.

Approximately two-thirds of major depressive disorder (MDD) patients have pain, which exacerbates the severity of depression. Electroconvulsive therapy (ECT) is an efficacious treatment that can alleviate depressive symptoms; however, treatment for pain and the underlying neural substrate is elusive. We enrolled 34 patients with MDD and 33 matched healthy controls to complete clinical assessments and neuroimaging scans. MDD patients underwent second assessments and scans after ECT. We defined a pain-related network with a published meta-analysis and calculated topological patterns to reveal topologic alterations induced by ECT. Using the amplitude of low-frequency fluctuations (ALFFs), we probed local function aberrations of pain-related circuits in MDD patients. Subsequently, we applied gray matter volume (GMV) to reveal structural alterations of ECT relieving pain. The relationships between functional and structural aberrations and pain were determined. ECT significantly alleviated pain. The neural mechanism based on pain-related circuits indicated that ECT weakened the circuit function (ALFF: left amygdala and right supplementary motor area), while augmenting the structure (GMV: bilateral amygdala/insula/hippocampus and anterior cingulate cortex). The topologic patterns became less efficient after ECT. Correlation analysis between the change in pain and GMV had negative results in bilateral amygdala/insula/hippocampus. Similarity, there was a positive correlation between a change in ALFF in the left amygdala and improved clinical symptoms. ECT improved pain by decreasing brain local function and global network patterns, while increasing structure in pain-related circuits. Functional and structural alterations were associated with improvement in pain.

Development of a novel MR-conditional microwave needle for MR-guided interventional microwave ablation at 1.5T.

PURPOSE: To develop an MR-conditional microwave needle that generates a spherical ablation zone and clear MRI visibility for MR-guided microwave ablation. METHODS: An MR-conditional microwave needle consisting of zirconia tip and TA18 titanium alloy tube was investigated. The numerical model was created to optimize the needle's geometry and analyze its performance. A geometrically optimized needle was produced using non-magnetic materials based on the electromagnetics simulation results. The needle's mechanical properties were tested per the Chinese pharmaceutical industry standard YY0899-2013. The MRI visibility performance and ablation characteristics of the needle was tested both in vitro (phantom) and in vivo (rabbit) at 1.5T. The RF-induced heating was evaluated in ex vivo porcine liver. RESULTS: The needle's mechanical properties met the specified requirements. The needle susceptibility artifact was clearly visible both in vitro and in vivo. The needle artifact diameter (A) was small in in vivo (Ashaft: 4.96 ± 0.18 mm for T1W-FLASH, 3.13 ± 0.05 mm for T2-weighted fast spin-echo (T2W-FSE); Atip: 2.31 ± 0.09 mm for T1W-FLASH, 2.29 ± 0.08 mm for T2W-FSE; tip location error [TLE]: -0.94 ± 0.07 mm for T1W-FLASH, -1.10 ± 0.09 mm for T2W-FSE). Ablation zones generated by the needle were nearly spherical with an elliptical aspect ratio ranging from 0.79 to 0.90 at 30 W, 50 W for 3, 5, 10 min duration ex vivo ablations and 0.86 at 30 W for 10 min duration in vivo ablations. CONCLUSION: The designed MR-conditional microwave needle offers excellent mechanical properties, reliable MRI visibility, insignificant RF-induced heating, and a sufficiently spherical ablation zone. Further clinical development of MR-guided microwave ablation appears warranted.

Glymphatic dysfunction correlates with severity of small vessel disease and cognitive impairment in cerebral amyloid angiopathy.

BACKGROUND AND PURPOSE: Cerebral amyloid angiopathy (CAA) is characterized by ß-amyloid deposition in cortical and leptomeningeal arterioles, which might result from glymphatic dysfunction. The aim was to explore glymphatic function in CAA using the non-invasive diffusion tensor image analysis along the perivascular space method. METHODS: Sixty-three patients with CAA were prospectively recruited together with seventy age- and sex-matched normal controls. The Mini-Mental State Examination and Montreal Cognitive Assessment were applied to screen global cognitive status. Magnetic resonance imaging scans were conducted to calculate the index for diffusivity along the perivascular space (ALPS index), and linear regression models were used to assess its relationships with cerebral small vessel disease (CSVD) markers, cognitive status and blood biomarkers. Cox proportional hazard models were applied to explore the role of the baseline ALPS index in disease recurrence. RESULTS: Patients with CAA exhibited a lower ALPS index than controls globally (p < 0.001). In addition, a lower ALPS index was related to more enlarged perivascular space in basal ganglia (p = 0.026), more lacunes (p < 0.001), higher white matter hyperintensity Fazekas score (p = 0.049), elevated total magnetic resonance imaging burden of CSVD (p = 0.034) and lower Mini-Mental State Examination (p = 0.001) as well as Montreal Cognitive Assessment (p < 0.001) in CAA. During a median follow-up of 4.1 years, a higher ALPS index was associated with lower disease recurrence (p = 0.022). The ALPS index was also negatively correlated with serum soluble intercellular adhesion molecule-1, neurofilament light and chitinase-3-like protein 1 in CAA. CONCLUSIONS: Patients with CAA showed impaired glymphatic function. The ALPS index was significantly related to CSVD severity, cognitive impairment and disease recurrence in CAA.

Alcohol dependence inpatients classification with GLM and hierarchical clustering integration using fMRI data of alcohol multiple scenario cues.

Alterations in brain reactions to alcohol-related cues are a neurobiological characteristic of alcohol dependence (AD) and a prospective target for achieving substantial treatment effects. However, a robust prediction of the differences in inpatients' brain responses to alcohol cues during the treatment process is still required. This study offers a data-driven approach for classifying AD inpatients undertaking alcohol treatment protocols based on their brain responses to alcohol imagery with and without drinking actions. The brain activity of thirty inpatients with AD undergoing treatment was scanned using functional magnetic resonance imaging (fMRI) while seeing alcohol and matched non-alcohol images. The mean values of brain regions of interest (ROI) for alcohol-related brain responses were obtained using general linear modeling (GLM) and subjected to hierarchical clustering analysis. The proposed classification technique identified two distinct subgroups of inpatients. For the two types of cues, subgroup one exhibited significant activation in a wide range of brain regions, while subgroup two showed mainly decreased activation. The proposed technique may aid in detecting the vulnerability of the classified inpatient subgroups, which can suggest allocating the inpatients in the classified subgroups to more effective therapies and developing prognostic future relapse markers in AD.

Longitudinal resting-state functional connectivity and regional brain atrophy-based biomarkers of preclinical cognitive impairment in healthy old adults.

BACKGROUND: Intervention against age-related neurodegenerative diseases may be difficult once extensive structural and functional deteriorations have already occurred in the brain. AIM: Investigating 6-year longitudinal changes and implications of regional brain atrophy and functional connectivity in the triple-network model as biomarkers of preclinical cognitive impairment in healthy aging. METHODS: We acquired longitudinal cognitive scores and magnetic resonance imaging (MRI) data from 74 healthy old adults. Resting-state functional MRI (rs-fMRI) analysis was conducted using FSL6.0.1 to examine functional connectivity changes and regional brain morphometries were quantified using FreeSurfer5.3. Finally, we cross-validated and compared two support vector machine (SVM) regression models to predict future 6-year cognition score from the baseline regional brain atrophy and resting-state functional connectivity (rs-FC) measures. RESULTS: After a 6-year follow-up, our results (P < 0.05-corrected) indicated significant connectivity reduction within all the three brain networks, significant differences in regional brain volumes and cortical thickness. We also observed significant improvement in episodic memory and significant decline in executive functions. Finally, comparing the two models, we observed that regional brain atrophy predictors were more efficient in approximating future 6-year cognitive scores (R = 0.756, P < 0.0001) than rs-FC predictors (R = 0.6, P < 0.0001). CONCLUSION: This study used longitudinal data to keep subject variability low and to increase the validity of the results. We demonstrated significant changes in structural and functional MRI over 6 years. Our findings present a potential neuroimaging-based biomarker to detect cognitive impairment and prevent risks of neurodegenerative diseases in healthy old adults.

White matter hyperintensities induce distal deficits in the connected fibers.

White matter hyperintensities (WMH) are common in elderly individuals and cause brain network deficits. However, it is still unclear how the global brain network is affected by the focal WMH. We aimed to investigate the diffusion of WMH-related deficits along the connecting white matters (WM). Brain magnetic resonance imaging data and neuropsychological evaluations of 174 participants (aged 74 ± 5 years) were collected and analyzed. For each participant, WMH lesions were segmented using a deep learning method, and 18 major WM tracts were reconstructed using automated quantitative tractography. The diffusion characteristics of distal WM tracts (with the WMH penumbra excluded) were calculated. Multivariable linear regression analysis was performed. We found that a high burden of tract-specific WMH was related to worse diffusion characteristics of distal WM tracts in a wide range of WM tracts, including the forceps major (FMA), forceps minor (FMI), anterior thalamic radiation (ATR), cingulum cingulate gyrus (CCG), corticospinal tract (CST), inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus-parietal (SLFP), superior longitudinal fasciculus-temporal (SLFT), and uncinate fasciculus (UNC). Furthermore, a higher mean diffusivity (MD) of distal tracts was linked to worse attention and executive function in the FMI, right CCG, left ILF, SLFP, SLFT, and UNC. The effect of WMH on the microstructural integrity of WM tracts may propagate along tracts to distal regions beyond the penumbra and might eventually affect attention and executive function.

Pre-supplementary motor network connectivity and clinical outcome of magnetic stimulation in obsessive-compulsive disorder.

A large proportion of patients with obsessive-compulsive disorder (OCD) respond unsatisfactorily to pharmacological and psychological treatments. An alternative novel treatment for these patients is repetitive transcranial magnetic stimulation (rTMS). This study aimed to investigate the underlying neural mechanism of rTMS treatment in OCD patients. A total of 37 patients with OCD were randomized to receive real or sham 1-Hz rTMS (14 days, 30 min/day) over the right pre-supplementary motor area (preSMA). Resting-state functional magnetic resonance imaging data were collected before and after rTMS treatment. The individualized target was defined by a personalized functional connectivity map of the subthalamic nucleus. After treatment, patients in the real group showed a better improvement in the Yale-Brown Obsessive Compulsive Scale than the sham group (F1,35  = 6.0, p = .019). To show the neural mechanism involved, we identified an "ideal target connectivity" before treatment. Leave-one-out cross-validation indicated that this connectivity pattern can significantly predict patients' symptom improvements (r = .60, p = .009). After real treatment, the average connectivity strength of the target network significantly decreased in the real but not in the sham group. This network-level change was cross-validated in three independent datasets. Altogether, these findings suggest that personalized magnetic stimulation on preSMA may alleviate obsessive-compulsive symptoms by decreasing the connectivity strength of the target network.

Dynamic brain connectome and high risk of mental problem in clinical nurses.

With the growing population and rapid change in the social environment, nurses in China are suffering from high rates of stress; however, the neural mechanism underlying this occupation related stress is largely unknown. In this study, mental status was determined for 81 nurses and 61 controls using the Symptom Checklist 90 (SCL-90) scale. A subgroup (n = 57) was further scanned by resting-state functional MRI with two sessions. Based on the SCL-90 scale, "somatic complaints" and "diet/sleeping" exhibited the most prominent difference between nurses and controls. This mental health change in nurses was further supported by the spatial independent component analysis on functional MRI data. First, dynamic functional connectome analysis identified two discrete connectivity configurations (States I and II). Controls had more time in the State I than II, while the nurses had more time in the State II than I. Second, nurses showed a similar static network topology as controls, but altered dynamic properties. Third, the symptom-imaging correlation analysis suggested the functional alterations in nurses as potential imaging biomarkers indicating a high risk for "diet/sleeping" problems. In summary, this study emphasized the high risk of mental deficits in nurses and explored the underlying neural mechanism using dynamic brain connectome, which provided valuable information for future psychological intervention.

Decline in executive function in patients with white matter hyperintensities from the static and dynamic perspectives of amplitude of low-frequency fluctuations.

Cognitive impairments are characteristics of patients with white matter hyperintensities (WMHs), and hypoperfusion is currently a relatively recognized mechanism of WMHs. Brain activity is closely coupled to the regulation of local blood flow. This study aimed to investigate the abnormal local brain activity of patients with WMHs from the viewpoint of the static amplitude of low-frequency fluctuations (sALFF) and dynamic amplitude of low-frequency fluctuations (dALFF). Seventy-four patients with WMHs and 35 healthy controls (HCs) were included. Based on the Fazekas scale, patients with WMHs were further divided into a mild WMH group (n = 33, Fazekas score 1-2) and moderate-severe WMH group (n = 41, Fazekas score 3-6). The sALFF and dALFF values were calculated separately and neuropsychological tests including the Montreal Cognitive Assessment (MoCA), Auditory Verbal Learning Test (AVLT), Trail Making Test (TMT), and Boston Naming Test (BNT) were completed by all participants. Patients with WMHs showed increased sALFF and dALFF values in the bilateral thalamus and decreased performance in the MoCA test, AVLT-immediate, AVLT-delay, AVLT-recognition, TMT-A, and BNT. The dALFF values in the bilateral thalamus was correlated with the MoCA in HCs. The sALFF values in the bilateral thalamus correlated with TMT-B in patients with WMHs. Patients with WMHs showed abnormal brain activity and decreased functional stability of the bilateral thalamus, which may be a potential mechanism of decreased executive function.

Decoding and mapping task states of the human brain via deep learning.

Support vector machine (SVM)-based multivariate pattern analysis (MVPA) has delivered promising performance in decoding specific task states based on functional magnetic resonance imaging (fMRI) of the human brain. Conventionally, the SVM-MVPA requires careful feature selection/extraction according to expert knowledge. In this study, we propose a deep neural network (DNN) for directly decoding multiple brain task states from fMRI signals of the brain without any burden for feature handcrafts. We trained and tested the DNN classifier using task fMRI data from the Human Connectome Project's S1200 dataset (N = 1,034). In tests to verify its performance, the proposed classification method identified seven tasks with an average accuracy of 93.7%. We also showed the general applicability of the DNN for transfer learning to small datasets (N = 43), a situation encountered in typical neuroscience research. The proposed method achieved an average accuracy of 89.0 and 94.7% on a working memory task and a motor classification task, respectively, higher than the accuracy of 69.2 and 68.6% obtained by the SVM-MVPA. A network visualization analysis showed that the DNN automatically detected features from areas of the brain related to each task. Without incurring the burden of handcrafting the features, the proposed deep decoding method can classify brain task states highly accurately, and is a powerful tool for fMRI researchers.

A single-cell identification and capture chip for automatically and rapidly determining hydraulic permeability of cells.

The hydraulic permeability of the lipid bilayer membrane of a single cell, a very important parameter in biological and medical fields, has been attracting increasing attention. To date, methods developed to determine this permeability are either operation-complicated or time-consuming. Therefore, we developed a chip for automatically and rapidly determining the permeability of cells that integrates microfluidics and cell impedance analysis. The chip is designed to automatically identify a single cell, capture the cell, and record the volume change in that cell. We confirmed the abilities of single-cell identification and capture with the upper and lower voltage thresholds determined, validated the performance of the differential electrode design for accurate cell volume measurements, deduced the extracellular osmotic pressure change in the presence of a hypertonic solution according to fluorescence intensity, and demonstrated the single-cell volume change recorded by the chip. Then, the accuracy of the permeability determined with the chip was verified using HeLa cells. Finally, the permeability of human-induced pluripotent stem cells (hiPSCs) was determined to be 0.47 ± 0.03 µm/atm/min. Using the chip, the permeability can be determined within 5 min. This study provides insights for the new design of an automatic single-cell identification and capture chip for single cell-related studies. Graphical abstract.

A common variant of the NOTCH4 gene modulates functional connectivity of the occipital cortex and its relationship with schizotypal traits.

BACKGROUND: Schizotypal traits are considered as inheritable traits and the endophenotype for schizophrenia. A common variant in the NOTCH4 gene, rs204993, has been linked with schizophrenia, but the neural underpinnings are largely unknown. METHODS: In present study, we compared the differences of brain functions between different genotypes of rs204993 and its relationship with schizotypal traits among 402 Chinese Han healthy volunteers. The brain function was evaluated with functional connectivity strength (FCS) using the resting-state functional magnetic resonance image(rs-fMRI). The schizotypal traits were measured by the schizotypal personality questionnaire (SPQ). RESULTS: Our results showed that carriers with the AA genotype showed reduced FCS in the left occipital cortex when compared with carriers with the AG and GG genotypes, and the carriers with the AG genotype showed reduced FCS in the left occipital cortex when compared with carriers with the GG genotype. The FCS values in the left occipital lobe were negatively associated with the SPQ scores and its subscale scores within the carriers with the GG genotype, but not within the carriers with AA or AG genotype. CONCLUSION: Our results suggested that the common variant in the NOTCH4 gene, rs204993, modulates the function of the occipital cortex, which may contribute to schizotypal traits. These findings provide insight for genetic effects on schizotypal traits and its potential neural substrate.

Increased Accuracy of Emotion Recognition in Individuals with Autism-Like Traits after Five Days of Magnetic Stimulations.

Individuals with autism-like traits (ALT) belong to a subclinical group with similar social deficits as autism spectrum disorders (ASD). Their main social deficits include atypical eye contact and difficulty in understanding facial expressions, both of which are associated with an abnormality of the right posterior superior temporal sulcus (rpSTS). It is still undetermined whether it is possible to improve the social function of ALT individuals through noninvasive neural modulation. To this end, we randomly assigned ALT individuals into the real (n = 16) and sham (n = 16) stimulation groups. All subjects received five consecutive days of intermittent theta burst stimulation (iTBS) on the rpSTS. Eye tracking data and functional magnetic resonance imaging (fMRI) data were acquired on the first and sixth days. The real group showed significant improvement in emotion recognition accuracy after iTBS, but the change was not significantly larger than that in the sham group. Resting-state functional connectivity (rsFC) between the rpSTS and the left cerebellum significantly decreased in the real group than the sham group after iTBS. At baseline, rsFC in the left cerebellum was negatively correlated with emotion recognition accuracy. Our findings indicated that iTBS of the rpSTS could improve emotion perception of ALT individuals by modulating associated neural networks. This stimulation protocol could be a vital therapeutic strategy for the treatment of ASD.

In vivo microscopic diffusional kurtosis imaging with symmetrized double diffusion encoding EPI.

PURPOSE: Diffusional kurtosis imaging (DKI) measures the deviation of the displacement probability from a normal distribution, complementing the data commonly acquired by diffusion MRI. It is important to elucidate the sources of kurtosis contrast, particularly in biological tissues where microscopic kurtosis (intrinsic kurtosis) and diffusional heterogeneity may co-exist. METHODS: We have developed a technique for microscopic kurtosis MRI, dubbed microscopic diffusional kurtosis imaging (µDKI), using a symmetrized double diffusion encoding (s-DDE) EPI sequence. We compared this newly developed µDKI to conventional DKI methods in both a triple compartment phantom and in vivo. RESULTS: Our results showed that whereas conventional DKI and µDKI provided similar measurements in a compartment of monosphere beads, kurtosis measured by µDKI was significantly less than that measured by conventional DKI in a compartment of mixed Gaussian pools. For in vivo brain imaging, µDKI showed small yet significantly lower kurtosis measurement in regions of the cortex, CSF, and internal capsule compared to the conventional DKI approach. CONCLUSIONS: Our study showed that µDKI is less susceptible than conventional DKI to sub-voxel diffusional heterogeneity. Our study also provided important preliminary demonstration of our technique in vivo, warranting future studies to investigate its diagnostic use in examining neurological disorders.