Principles of diffusion kurtosis imaging and its role in early diagnosis of neurodegenerative disorders

Epileptic brain network mechanisms and neuroimaging techniques for the brain network

Epilepsy can be defined as a dysfunction of the brain network, and each type of epilepsy involves different brain-network changes that are implicated differently in the control and propagation of interictal or ictal discharges. Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice. An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tractography, diffusion kurtosis imaging-based fiber tractography, fiber ball imaging-based tractography, electroencephalography, functional magnetic resonance imaging, magnetoencephalography, positron emission tomography, molecular imaging, and functional ultrasound imaging have been extensively used to delineate epileptic networks. In this review, we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy, and extensively analyze the imaging mechanisms, advantages, limitations, and clinical application ranges of each technique. A greater focus on emerging advanced technologies, new data analysis software, a combination of multiple techniques, and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.

The role of language-related functional brain regions and white matter tracts in network plasticity of post-stroke aphasia

The neural mechanisms underlying language recovery after a stroke remain controversial. This review aimed to summarize the plasticity and reorganization mechanisms of the language network through neuroimaging studies. Initially, we discussed the involvement of right language homologues, perilesional tissue, and domain-general networks. Subsequently, we summarized the white matter functional mapping and remodeling mechanisms associated with language subskills. Finally, we explored how non-invasive brain stimulation (NIBS) promoted language recovery by inducing neural network plasticity. It was observed that the recruitment of right hemisphere language area homologues played a pivotal role in the early stages of frontal post-stroke aphasia (PSA), particularly in patients with larger lesions. Perilesional plasticity correlated with improved speech performance and prognosis. The domain-general networks could respond to increased "effort" in a task-dependent manner from the top-down when the downstream language network was impaired. Fluency, repetition, comprehension, naming, and reading skills exhibited overlapping and unique dual-pathway functional mapping models. In the acute phase, the structural remodeling of white matter tracts became challenging, with recovery predominantly dependent on cortical activation. Similar to the pattern of cortical activation, during the subacute and chronic phases, improvements in language functions depended, respectively, on the remodeling of right white matter tracts and the restoration of left-lateralized language structural network patterns. Moreover, the midline superior frontal gyrus/dorsal anterior cingulate cortex emerged as a promising target for NIBS. These findings offered theoretical insights for the early personalized treatment of aphasia after stroke.

The impact of submandibular glands protection on xerostomia as monitored by diffusion-weighted imaging in nasopharyngeal carcinoma patients

PURPOSE

To determine the impact of sparing submandibular glands (SMGs) on alleviating xerostomia and the functional dynamics of the irradiated parotid glands (PGs) and sublingual glands (SLGs) by diffusion-weighted imaging.

METHODS

97 participants underwent 9 rounds of DWI scans before IC (pre-IC), pre-radiation (pre-RT), the midpoint of radiation (mid-RT), the end of radiation (post-RT), 1, 3, 6, 9, 12 (12m-RT) months following radiation. Apparent diffusion coefficient of SMGs (ADCSMG), PGs (ADCPG), and SLGs (ADCSLG), xerostomia questionnaire scores (XQ), and saliva flow rate measures under unstimulated (uSFR) and stimulated condition (sSFR) were documented.

RESULTS

ADCPG, ADCSMG, ADCSLG, and XQ showed a rapid increase with a top at 3m-RT followed by regression, whereas uSFR and sSFR had the reverse trend. The change rate of ADC correlated with the dose to PGs, SMGs, and SLGs, as well as uSFR, sSFR, and XQ scores (p < 0.05 for all, except for uSFR with ADCPG (p = 0.063)). Maingroup for ADCPG, uSFR, and sSFR were significant (p values were 0.028, 0.000, 0.000 respectively); ADCPG in SMG sparing group was lower while uSFR, and sSFR were higher than those in the SMG-unsparing group. Simplegroup for ADCSMG, ADCSLG (all p < 0.05 from mid-RT to 12m-RT), and XQ (all p < 0.001 at mid-, 6m-, 9m-, and 12m-RT) were significant; ADCSMG, ADCSLG, and XQ were lower in the SMG-sparing group.

CONCLUSIONS

SMG protection has a great impact on the functional retention of PGs and SLGs, resulting in alleviating xerostomia and improving quality of life.

TRIAL REGISTRATION

The clinical trial was also registered with the Chinese Clinical Study Registry (registered number: ChiCTR1900024328, Date: July 6, 2019; URL: https://www.chictr.org.cn/showproj.aspx?proj=40726 ).

Elossaily G, Vellapandian C, Prajapati B. Investigating the Potential Impact of Air Pollution on Alzheimer's Disease and the Utility of Multidimensional Imaging for Early Detection

Pollution is ubiquitous, and much of it is anthropogenic in nature, which is a severe risk factor not only for respiratory infections or asthma sufferers but also for Alzheimer's disease, which has received a lot of attention recently. This Review aims to investigate the primary environmental risk factors and their profound impact on Alzheimer's disease. It underscores the pivotal role of multidimensional imaging in early disease identification and prevention. Conducting a comprehensive review, we delved into a plethora of literature sources available through esteemed databases, including Science Direct, Google Scholar, Scopus, Cochrane, and PubMed. Our search strategy incorporated keywords such as "Alzheimer Disease", "Alzheimer's", "Dementia", "Oxidative Stress", and "Phytotherapy" in conjunction with "Criteria Pollutants", "Imaging", "Pathology", and "Particulate Matter". Alzheimer's disease is not only a result of complex biological factors but is exacerbated by the infiltration of airborne particles and gases that surreptitiously breach the nasal defenses to traverse the brain, akin to a Trojan horse. Various imaging modalities and noninvasive techniques have been harnessed to identify disease progression in its incipient stages. However, each imaging approach possesses inherent limitations, prompting exploration of a unified technique under a single umbrella. Multidimensional imaging stands as the linchpin for detecting and forestalling the relentless march of Alzheimer's disease. Given the intricate etiology of the condition, identifying a prospective candidate for Alzheimer's disease may take decades, rendering the development of a multimodal imaging technique an imperative. This research underscores the pressing need to recognize the chronic ramifications of invisible particulate matter and to advance our understanding of the insidious environmental factors that contribute to Alzheimer's disease.

Application of diffusional kurtosis imaging for insights into structurally aberrant topology in Parkinson's disease

BACKGROUND

Parkinson's disease (PD) has been regarded as a disconnection syndrome with functional and structural disturbances. However, as the anatomic determinants, the structural disconnections in PD have yet to be fully elucidated.

PURPOSE

To non-invasively construct structural networks based on microstructural complexity and to further investigate their potential topological abnormalities in PD given the technical superiority of diffusion kurtosis imaging (DKI) to the quantification of microstructure.

MATERIAL AND METHODS

The microstructural data of gray matter in both the PD group and the healthy control (HC) group were acquired using DKI. The structural networks were constructed at the group level by a covariation approach, followed by the calculation of topological properties based on graph theory and statistical comparisons between groups.

RESULTS

A total of 51 patients with PD and 50 HCs were enrolled. Individuals were matched between groups with respect to demographic characteristics (P >0.05). The constructed structural networks in both the PD and HC groups featured small-world properties. In comparison with the HC group, the PD group exhibited significantly altered global properties, with higher normalized characteristic path lengths, clustering coefficients, local efficiency values, and characteristic path lengths and lower global efficiency values (P <0.05). In terms of nodal centralities, extensive nodal disruptions were observed in patients with PD (P <0.05); these disruptions were mainly distributed in the sensorimotor network, default mode network, frontal-parietal network, visual network, and subcortical network.

CONCLUSION

These findings contribute to the technical application of DKI and the elucidation of disconnection syndrome in PD.

White Matter Microstructure Analysis in Subjective Memory Complaints and Cognitive Impairment: Insights from Diffusion Kurtosis Imaging and Free-Water DTI

Background

Dementia is characterized by a cognitive decline in memory and other domains that lead to functional impairments. As people age, subjective memory complaints (SMC) become common, where individuals perceive cognitive decline without objective deficits on assessments. SMC can be an early sign and may precede amnestic mild cognitive impairment (MCI), which frequently advances to Alzheimer's disease (AD).

Objective

This study aims to investigate white matter microstructure in individuals with SMC, in cognitively impaired (CI) cohorts, and in cognitively normal individuals using diffusion kurtosis imaging (DKI) and free water imaging (FWI). The study also explores voxel-based correlations between DKI/FWI metrics and cognitive scores to understand the relationship between brain microstructure and cognitive function.

Methods

Twelve healthy controls (HCs), ten individuals with SMC, and eleven CI individuals (MCI or AD) were enrolled in this study. All participants underwent MRI 3T scan and the BNI Screen (BNIS) for Higher Cerebral Functions.

Results

The mean kurtosis tensor and anisotropy of the kurtosis tensor showed significant differences across the three groups, indicating altered white matter microstructure in CI and SMC individuals. The free water volume fraction (f) also revealed group differences, suggesting changes in extracellular water content. Notably, these metrics effectively discriminated between the CI and HC/SMC groups. Additionally, correlations between imaging metrics and BNIS scores were found for CI and SMC groups.

Conclusions

These imaging metrics hold promise in discriminating between individuals with CI and SMC. The observed differences indicate their potential as sensitive and specific biomarkers for early detection and differentiation of cognitive decline.

Histogram analysis of diffusion kurtosis imaging of deep brain nuclei in Parkinson's disease with different motor subtypes

AIM

To evaluate the diagnostic and differential efficacy of diffusion kurtosis imaging (DKI) histogram analysis for different motor subtypes of Parkinson's disease (PD).

MATERIALS AND METHODS

Seventy PD patients including 40 with postural instability and gait disorder (PIGD) and 30 with tremor-dominant (TD) and 36 healthy controls (HC) were enrolled prospectively and underwent MRI examinations. The regions of interest (ROI) in the deep brain nuclei were delineated and features were extracted on the map of mean kurtosis (MK), axial kurtosis (Ka), and radial kurtosis (Kr), respectively. The differences in histogram features between PD patients and HC and between patients with PIGD and TD were compared. The areas under the curve (AUCs) were calculated to evaluate the diagnostic efficacy of all histogram features. The correlations between histogram features and clinical indicators were evaluated.

RESULTS

Some DKI histogram features were significantly different between PD patients and HC, and also different between patients with PIGD and TD (all p<0.05). MK of the substantia nigra pars reticulate (SNprkurtosis), Ka of the substantia nigra pars compacta (SNpc) 50 percentile (SNpcP50), and Kr of SNpc 90th percentile showed the highest AUC for distinguishing patients with PIGD from HC. MK-SNpc 10th percentile, Ka-SNpc 25th percentile, and Kr of the head of the caudate nucleus (CN) 90th percentile had the highest AUC for distinguishing patients with TD from HC. MK of the putamen 10th percentile combined with Ka of the bilateral red nucleus RNkurtosis yielded the highest diagnostic performance with an AUC of 0.762 for distinguishing patients with PIGD from TD. Certain DKI histogram features were correlated with Hoehn-Yahr (H&Y) stage, Mini Mental State Examination (MMSE) score, tremor score, and PIGD score (all p<0.05).

CONCLUSION

DKI histogram analysis was useful to diagnose and discriminate different motor subtypes of PD. Certain DKI histogram features correlated with clinical indicators.

White matter changes in Parkinson's disease

Parkinson's disease (PD) is the second most common neurodegenerative disease after Alzheimer's disease (AD). It is characterized by a progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc) and the formation of Lewy bodies (LBs). Although PD is primarily considered a gray matter (GM) disease, alterations in white matter (WM) have gained increasing attention in PD research recently. Here we review evidence collected by magnetic resonance imaging (MRI) techniques which indicate WM abnormalities in PD, and discuss the correlations between WM changes and specific PD symptoms. Then we summarize transcriptome and genome studies showing the changes of oligodendrocyte (OLs)/myelin in PD. We conclude that WM abnormalities caused by the changes of myelin/OLs might be important for PD pathology, which could be potential targets for PD treatment.

Current and Emerging Techniques in Neuroimaging of Sport-Related Concussion

SUMMARY

Sport-related concussion (SRC) affects an estimated 1.6 to 3.8 million Americans each year. Sport-related concussion results from biomechanical forces to the head or neck that lead to a broad range of neurologic symptoms and impaired cognitive function. Although most individuals recover within weeks, some develop chronic symptoms. The heterogeneity of both the clinical presentation and the underlying brain injury profile make SRC a challenging condition. Adding to this challenge, there is also a lack of objective and reliable biomarkers to support diagnosis, to inform clinical decision making, and to monitor recovery after SRC. In this review, the authors provide an overview of advanced neuroimaging techniques that provide the sensitivity needed to capture subtle changes in brain structure, metabolism, function, and perfusion after SRC. This is followed by a discussion of emerging neuroimaging techniques, as well as current efforts of international research consortia committed to the study of SRC. Finally, the authors emphasize the need for advanced multimodal neuroimaging to develop objective biomarkers that will inform targeted treatment strategies after SRC.

Neuroimaging in early-treated phenylketonuria patients and clinical outcome: A systematic review

Lacking direct neuropathological data, neuroimaging exploration has become the most powerful tool to give insight into pathophysiological alterations of early-treated PKU (ETPKU) patients. We conducted a systematic review of neuroimaging studies in ETPKU patients to explore 1) the occurrence of consistent neuroimaging alterations; 2) the relationship between them and neurological and cognitive disorders; 3) the contribution of neuroimaging in the insight of neuropathological background of ETPKU subjects; 4) whether brain neuroimaging may provide additional information in the monitoring of the disease course. Thirty-eight studies met the inclusion criteria for the full-text review, including morphological T1/T2 sequences, diffusion brain imaging (DWI/DTI) studies, brain MRI volumetric, functional neuroimaging studies, neurotransmission and brain energetic imaging studies. Non-progressive brain white matter changes were the most frequent and precocious alterations. As confirmed in hundreds of young adults with ETPKU, they affect over 90% of ETPKU patients. Consistent correlations are emerging between microstructural alteration (as detected by DWI/DTI) and metabolic control, which have also been confirmed in a few interventional trials. Volumetric studies detected later and less consistent cortical and subcortical grey matter alterations, which seem to be influenced by the patient's age and metabolic control. The few functional neuroimaging studies so far showed preliminary but interesting data about cortical activation patterns, skill performance, and brain connectivity. Further research is mandatory in these more complex areas. Recurrent methodological limitations include restricted sample sizes concerning the clinical variability of the disease, large age-range, variable measures of metabolic control, and prevalence of cross-sectional rather than longitudinal interventional studies.

The Use of Diffusion Kurtosis Imaging for the Differential Diagnosis of Alzheimer’s Disease Spectrum

Structural and diffusion kurtosis imaging (DKI) can be used to assess hippocampal macrostructural and microstructural alterations respectively, in Alzheimer’s disease (AD) spectrum, spanning from subjective cognitive decline (SCD) to mild cognitive impairment (MCI) and AD. In this study, we explored the diagnostic performance of structural imaging and DKI of the hippocampus in the AD spectrum. Eleven SCD, thirty-seven MCI, sixteen AD, and nineteen age- and sex-matched normal controls (NCs) were included. Bilateral hippocampal volume, mean diffusivity (MD), and mean kurtosis (MK) were obtained. We detected that in AD vs. NCs, the right hippocampal volume showed the most prominent AUC value (AUC = 0.977); in MCI vs. NCs, the right hippocampal MD was the most sensitive discriminator (AUC = 0.819); in SCD vs. NCs, the left hippocampal MK was the most sensitive biomarker (AUC = 0.775). These findings suggest that, in the predementia stage (SCD and MCI), hippocampal microstructural changes are predominant, and the best discriminators are microstructural measurements (left hippocampal MK for SCD and right hippocampal MD for MCI); while in the dementia stage (AD), hippocampal macrostructural alterations are superior, and the best indicator is the macrostructural index (right hippocampal volume).

Role of diffusion kurtosis imaging in evaluating microstructural changes in spinal cord of patients with cervical spondylosis

STUDY DESIGN

Analytical cross-sectional study.

PURPOSE

To study the role of diffusion kurtosis imaging (DKI) in evaluating microstructural changes in patients with cervical spondylosis.

OVERVIEW OF LITERATURE

Cervical spondylosis is a common progressive degenerative disorder of the spine. Conventional magnetic resonance imaging (MRI) can only detect the changes in the spinal cord once there are visual signal changes; hence, it underestimates the extent of the injury. Newer imaging techniques like Diffusion Tensor and Kurtosis Imaging can evaluate the microstructural changes in cervical spinal cord before the obvious signal changes appear.

METHODS

Conventional MRI, diffusion tensor imaging (DTI), and DKI scans were performed for 90 cervical spondylosis patients on 1.5-T MR Siemens Magnetom aera after obtaining informed consent. Eight patients were excluded due to poor image quality. Fractional anisotropy (FA) colour maps and diffusion kurtosis (DK) maps corresponding to spinal cord cross sections at C2-C3 intervertebral disc level (control) and at the most stenotic levels were obtained. Modified Japanese Orthopaedic Association (mJOA) scoring was used for clinical assessment of the spinal cord function. The changes in DTI and DKI parameters and their correlation with mJOA scores were analysed by SPSS 23 software.

RESULTS

In our study, mean FA and mean kurtosis (MK) values at the stenotic level (0.54, 1.02) were significantly lower than values at the non-stenotic segment (0.70, 1.27). The mean diffusivity (MD) value at the stenotic segment (1.25) was significantly higher than in the non-stenotic segment (1.09). We also observed a strong positive correlation between mJOA score and FA and MK values and a negative correlation between mJOA score and MD values, suggesting a correlation of FA, MK, and MD with the clinical severity of the disease.

CONCLUSION

Addition of DTI and DKI sequences helps in early identification of the disease without any additional cost incurred by the patient.

Applications of advanced diffusion MRI in early brain development: a comprehensive review

Brain development follows a protracted developmental timeline with foundational processes of neurodevelopment occurring from the third trimester of gestation into the first decade of life. Defining structural maturational patterns of early brain development is a critical step in detecting divergent developmental trajectories associated with neurodevelopmental and psychiatric disorders that arise later in life. While considerable advancements have already been made in diffusion magnetic resonance imaging (dMRI) for pediatric research over the past three decades, the field of neurodevelopment is still in its infancy with remarkable scientific and clinical potential. This comprehensive review evaluates the application, findings, and limitations of advanced dMRI methods beyond diffusion tensor imaging, including diffusion kurtosis imaging (DKI), constrained spherical deconvolution (CSD), neurite orientation dispersion and density imaging (NODDI) and composite hindered and restricted model of diffusion (CHARMED) to quantify the rapid and dynamic changes supporting the underlying microstructural architectural foundations of the brain in early life.

Diffusion Imaging of Sport-related Repetitive Head Impacts-A Systematic Review

Repetitive head impacts (RHI) are commonly observed in athletes participating in contact sports such as American football, ice hockey, and soccer. RHI usually do not result in acute symptoms and are therefore often referred to as subclinical or "subconcussive" head impacts. Epidemiological studies report an association between exposure to RHI and an increased risk for the development of neurodegenerative diseases. Diffusion magnetic resonance imaging (dMRI) has emerged as particularly promising for the detection of subtle alterations in brain microstructure following exposure to sport-related RHI. The purpose of this study was to perform a systematic review of studies investigating the effects of exposure to RHI on brain microstructure using dMRI. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to determine studies that met inclusion and exclusion criteria across three databases. Seventeen studies were identified and critically evaluated. Results from these studies suggest an association between white matter alterations and RHI exposure in youth and young adult athletes. The most consistent finding across studies was lower or decreased fractional anisotropy (FA), a measure of the directionality of the diffusion of water molecules, associated with greater exposure to sport-related RHI. Whether decreased FA is associated with functional outcome (e.g., cognition) in those exposed to RHI is yet to be determined. This review further identified areas of importance for future research to increase the diagnostic and prognostic value of dMRI in RHI and to improve our understanding of the effects of RHI on brain physiology and microstructure.

Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology

Glioblastoma (GBM) is a malignant brain tumor exhibiting rapid and infiltrative growth, with less than 10% of patients surviving over 5 years, despite aggressive and multimodal treatments. The poor prognosis and the lack of effective pharmacological treatments are imputable to a remarkable histological and molecular heterogeneity of GBM, which has led, to date, to the failure of precision oncology and targeted therapies. Identification of molecular biomarkers is a paradigm for comprehensive and tailored treatments; nevertheless, biopsy sampling has proved to be invasive and limited. Radiogenomics is an emerging translational field of research aiming to study the correlation between radiographic signature and underlying gene expression. Although a research field still under development, not yet incorporated into routine clinical practice, it promises to be a useful non-invasive tool for future personalized/adaptive neuro-oncology. This review provides an up-to-date summary of the recent advancements in the use of magnetic resonance imaging (MRI) radiogenomics for the assessment of molecular markers of interest in GBM regarding prognosis and response to treatments, for monitoring recurrence, also providing insights into the potential efficacy of such an approach for survival prognostication. Despite a high sensitivity and specificity in almost all studies, accuracy, reproducibility and clinical value of radiomic features are the Achilles heel of this newborn tool. Looking into the future, investigators' efforts should be directed towards standardization and a disciplined approach to data collection, algorithms, and statistical analysis.

Neuroimaging-Derived Biomarkers of the Antidepressant Effects of Ketamine

Major depressive disorder is a highly prevalent psychiatric disorder. Despite an extensive range of treatment options, about a third of patients still struggle to respond to available therapies. In the last 20 years, ketamine has gained considerable attention in the psychiatric field as a promising treatment of depression, particularly in patients who are treatment resistant or at high risk for suicide. At a subanesthetic dose, ketamine produces a rapid and pronounced reduction in depressive symptoms and suicidal ideation, and serial treatment appears to produce a greater and more sustained therapeutic response. However, the mechanism driving ketamine's antidepressant effects is not yet well understood. Biomarker discovery may advance knowledge of ketamine's antidepressant action, which could in turn translate to more personalized and effective treatment strategies. At the brain systems level, neuroimaging can be used to identify functional pathways and networks contributing to ketamine's therapeutic effects by studying how it alters brain structure, function, connectivity, and metabolism. In this review, we summarize and appraise recent work in this area, including 51 articles that use resting-state and task-based functional magnetic resonance imaging, arterial spin labeling, positron emission tomography, structural magnetic resonance imaging, diffusion magnetic resonance imaging, or magnetic resonance spectroscopy to study brain and clinical changes 24 hours or longer after ketamine treatment in populations with unipolar or bipolar depression. Though individual studies have included relatively small samples, used different methodological approaches, and reported disparate regional findings, converging evidence supports that ketamine leads to neuroplasticity in structural and functional brain networks that contribute to or are relevant to its antidepressant effects.

The mean kurtosis (MK) is more sensitive diagnostic biomarker than fractional anisotropy (FA) for Parkinson's disease: A diagnostic performance study and meta-analysis

BACKGROUND

The mean kurtosis (MK) and fractional anisotropy (FA) in patients of Parkinson's disease (PD) are usually measured by diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI), separately.

METHODS

In this study we perform a meta-analysis to discuss which noninvasive biomarker is more advantageous for PD, MK, or FA. Databases including Medline via PubMed, the Cochrane Central Register of Controlled Trials, Embase via OVID and China National Knowledge Infrastructure. Databases are searched up to December 31st, 2019. Four brain regions are identified for analysis based on data extracted from articles.

RESULTS

The articles contain 5 trials with 274 total PD patients and 189 healthy controls (HCs). The results show not only significantly higher MK values of putamen, caudate, globus pallidus in PD compared to that of HCs (weighted mean difference [WMD] = 0.06, 95% CI = 0.02-0.09, P = .002, WMD = 0.03, 95% CI = 0.01-0.067, P = .01, WMD = 0.18, 95% CI = 0.11-0.24, P < .00001), but also a significantly higher FA in caudate of PD compared to HCs (WMD = 0.02, 95% CI = 0.00-0.03, P = .006).

CONCLUSION

This indicates that the sharp difference detected between PD patients and HCs can be detected by DKI and DTI. By further discussing results, we found that MK could be more sensitive diagnostic biomarker than FA toward PD diagnosis.

A comparative study of functional MRI in predicting response of regional nodes to induction chemotherapy in patients with nasopharyngeal carcinoma

Purpose

To identify and compare the value of functional MRI (fMRI) in predicting the early response of metastatic cervical lymph nodes (LNs) to induction chemotherapy (IC) in nasopharyngeal carcinoma (NPC) patients.

Methods

This prospective study collected 94 metastatic LNs from 40 consecutive NPC patients treated with IC from January 2021 to May 2021. Conventional diffusion-weighted imaging, diffusion kurtosis imaging, intravoxel incoherent motion, and dynamic contrast-enhanced magnetic resonance imaging were performed before and after IC. The parameter maps apparent diffusion coefficient (ADC), mean diffusion coefficient (MD), mean kurtosis (MK), Dslow, Dfast, perfusion fraction (PF), Ktrans, Ve, and Kep) of the metastatic nodes were calculated by the Functool postprocessing software. All LNs were classified as the responding group (RG) and non-responding group (NRG) according to Response Evaluation Criteria in Solid Tumors 1.1. The fMRI parameters were compared before and after IC and between the RG and the NRG. The significant parameters are fitted by logistic regression analysis to produce new predictive factor (PRE)–predicted probabilities. Logistic regression analysis and receiver operating characteristic (ROC) curves were performed to further identify and compare the efficacy of the parameters.

Results

After IC, the mean values of ADC, MD, and Dslow significantly increased, while MK, Dfast, and Ktrans values decreased dramatically, while no significant difference was detected in Ve and Kep. Compared with NRG, PF-pre and Ktrans-pre values in the RG were higher statistically. The areas under the ROC for the pretreatment PF, Ktrans, and PRE were 0.736, 0.722, and 0.810, respectively, with the optimal cutoff value of 222 × 10-4, 934 × 10-3/min, and 0.6624, respectively.

Conclusions

The pretreatment fMRI parameters PF and Ktrans showed promising potential in predicting the response of the metastatic LNs to IC in NPC patients.

Clinical Trial Registration

This study was approved by the ethics board of the Chinese PLA General Hospital, and registered on 30 January 2021, in the Chinese Clinical Trial Registry; http://www.chictr.org.cn/showproj.aspx?proj=121198, identifier (ChiCTR2100042863).

Comparison of brain microstructure alterations on diffusion kurtosis imaging among Alzheimer’s disease, mild cognitive impairment, and cognitively normal individuals

Objective

Our study aimed to explore the differences in brain microstructure in patients with Alzheimer’s disease (AD) and with mild cognitive impairment (MCI) and in individuals with normal cognition using diffusion kurtosis imaging (DKI) to identify a potential non-invasive biomarker of AD.

Materials and methods

A total of 61 subjects were included in our study, including 20 subjects diagnosed with AD, 21 patients diagnosed with amnestic MCI, and 20 cognitively normal individuals. We acquired magnetic resonance imaging (MRI) scans, and DKI images were processed. Twelve regions of interest were drawn, and various parameters were measured and analyzed using SPSS version 11.0 software.

Results

Comparative analysis showed that differences in brain regions in terms of mean diffusion (MD) and mean kurtosis (MK) between groups were the most marked. Precuneus MD, temporal MK, precuneus MK, and hippocampal MK were significantly correlated with neuropsychological test scores. Hippocampal MK showed the strongest correlation with the medial temporal lobe atrophy score (r = −0.510), and precuneus MD had the strongest correlation with the Koedam score (r = 0.463). The receiver operating curve analysis revealed that hippocampal MK exhibited better diagnostic efficacy than precuneus MD for comparisons between any group pair.

Conclusion

DKI is capable of detecting differences in brain microstructure between patients with AD, patients with MCI, and cognitively normal individuals. Moreover, it compensates for the deficiencies of conventional MRI in detecting pathological changes in microstructure before the appearance of macroscopic atrophy. Hippocampus MK was the most sensitive single parameter map for differentiating patients with AD, patients with MCI, and cognitively normal individuals.

Cross-site harmonization of multi-shell diffusion MRI measures based on rotational invariant spherical harmonics (RISH)

Quantification methods based on the acquisition of diffusion magnetic resonance imaging (dMRI) with multiple diffusion weightings (e.g., multi-shell) are becoming increasingly applied to study the in-vivo brain. Compared to single-shell data for diffusion tensor imaging (DTI), multi-shell data allows to apply more complex models such as diffusion kurtosis imaging (DKI), which attempts to capture both diffusion hindrance and restriction effects, or biophysical models such as NODDI, which attempt to increase specificity by separating biophysical components. Because of the strong dependence of the dMRI signal on the measurement hardware, DKI and NODDI metrics show scanner and site differences, much like other dMRI metrics. These effects limit the implementation of multi-shell approaches in multicenter studies, which are needed to collect large sample sizes for robust analyses. Recently, a post-processing technique based on rotation invariant spherical harmonics (RISH) features was introduced to mitigate cross-scanner differences in DTI metrics. Unlike statistical harmonization methods, which require repeated application to every dMRI metric of choice, RISH harmonization is applied once on the raw data, and can be followed by any analysis. RISH features harmonization has been tested on DTI features but not its generalizability to harmonize multi-shell dMRI. In this work, we investigated whether performing the RISH features harmonization of multi-shell dMRI data removes cross-site differences in DKI and NODDI metrics while retaining longitudinal effects. To this end, 46 subjects underwent a longitudinal (up to 3 time points) two-shell dMRI protocol at 3 imaging sites. DKI and NODDI metrics were derived before and after harmonization and compared both at the whole brain level and at the voxel level. Then, the harmonization effects on cross-sectional and on longitudinal group differences were evaluated. RISH features averaged for each of the 3 sites exhibited prominent between-site differences in the frontal and posterior part of the brain. Statistically significant differences in fractional anisotropy, mean diffusivity and mean kurtosis were observed both at the whole brain and voxel level between all the acquisition sites before harmonization, but not after. The RISH method also proved effective to harmonize NODDI metrics, particularly in white matter. The RISH based harmonization maintained the magnitude and variance of longitudinal changes as compared to the non-harmonized data of all considered metrics. In conclusion, the application of RISH feature based harmonization to multi-shell dMRI data can be used to remove cross-site differences in DKI metrics and NODDI analyses, while retaining inherent relations between longitudinal acquisitions.

Application of Quantitative Magnetic Resonance Imaging in the Diagnosis of Autism in Children

Objective

To explore the application of quantitative magnetic resonance imaging in the diagnosis of autism in children.

Methods

Sixty autistic children aged 2–3 years and 60 age- and sex-matched healthy children participated in the study. All the children were scanned using head MRI conventional sequences, 3D-T1, diffusion kurtosis imaging (DKI), enhanced T2*- weighted magnetic resonance angiography (ESWAN) and 3D-pseudo continuous Arterial Spin-Labeled (3D-pcASL) sequences. The quantitative susceptibility mapping (QSM), cerebral blood flow (CBF), and brain microstructure of each brain area were compared between the groups, and correlations were analyzed.

Results

The iron content and cerebral blood flow in the frontal lobe, temporal lobe, hippocampus, caudate nucleus, substantia nigra, and red nucleus of the study group were lower than those in the corresponding brain areas of the control group (P < 0.05). The mean kurtosis (MK), radial kurtosis (RK), and axial kurtosis (AK) values of the frontal lobe, temporal lobe, putamen, hippocampus, caudate nucleus, substantia nigra, and red nucleus in the study group were lower than those of the corresponding brain areas in the control group (P < 0.05). The mean diffusivity (MD) and fractional anisotropy of kurtosis (FAK) values of the frontal lobe, temporal lobe and hippocampus in the control group were lower than those in the corresponding brain areas in the study group (P < 0.05). The values of CBF, QSM, and DKI in frontal lobe, temporal lobe and hippocampus could distinguish ASD children (AUC > 0.5, P < 0.05), among which multimodal technology (QSM, CBF, DKI) had the highest AUC (0.917) and DKI had the lowest AUC (0.642).

Conclusion

Quantitative magnetic resonance imaging (including QSM, 3D-pcASL, and DKI) can detect abnormalities in the iron content, cerebral blood flow and brain microstructure in young autistic children, multimodal technology (QSM, CBF, DKI) could be considered as the first choice of imaging diagnostic technology.

Clinical Trial Registration

[http://www.chictr.org.cn/searchprojen.aspx], identifier [ChiCTR2000029699].

Machine Learning Based on Diffusion Kurtosis Imaging Histogram Parameters for Glioma Grading

Glioma grading plays an important role in surgical resection. We investigated the ability of different feature reduction methods in support vector machine (SVM)-based diffusion kurtosis imaging (DKI) histogram parameters to distinguish glioma grades. A total of 161 glioma patients who underwent magnetic resonance imaging (MRI) from January 2017 to January 2020 were included retrospectively. The patients were divided into low-grade (n = 61) and high-grade (n = 100) groups. Parametric DKI maps were derived, and 45 features from the DKI maps were extracted semi-automatically for analysis. Three feature selection methods [principal component analysis (PCA), recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO)] were used to establish the glioma grading model with an SVM classifier. To evaluate the performance of SVM models, the receiver operating characteristic (ROC) curves of SVM models for distinguishing glioma grades were compared with those of conventional statistical methods. The conventional ROC analysis showed that mean diffusivity (MD) variance, MD skewness and mean kurtosis (MK) C50 could effectively distinguish glioma grades, particularly MD variance. The highest classification distinguishing AUC was found using LASSO at 0.904 ± 0.069. In comparison, classification AUC by PCA was 0.866 ± 0.061, and 0.899 ± 0.079 by RFE. The SVM-PCA model with the lowest AUC among the SVM models was significantly better than the conventional ROC analysis (z = 1.947, p = 0.013). These findings demonstrate the superiority of DKI histogram parameters by LASSO analysis and SVM for distinguishing glioma grades.

In vivo Correlation Tensor MRI reveals microscopic kurtosis in the human brain on a clinical 3T scanner

Diffusion MRI (dMRI) has become one of the most important imaging modalities for noninvasively probing tissue microstructure. Diffusional Kurtosis MRI (DKI) quantifies the degree of non-gaussian diffusion, which in turn has been shown to increase sensitivity towards, e.g., disease and orientation mapping in neural tissue. However, the specificity of DKI is limited as different sources can contribute to the total intravoxel diffusional kurtosis, including: variance in diffusion tensor magnitudes (K_iso), variance due to diffusion anisotropy (K_aniso), and microscopic kurtosis (μK) related to restricted diffusion, microstructural disorder, and/or exchange. Interestingly, μK is typically ignored in diffusion MRI signal modeling as it is assumed to be negligible in neural tissues. However, recently, Correlation Tensor MRI (CTI) based on Double-Diffusion-Encoding (DDE) was introduced for kurtosis source separation, revealing non negligible μK in preclinical imaging. Here, we implemented CTI for the first time on a clinical 3T scanner and investigated the sources of total kurtosis in healthy subjects. A robust framework for kurtosis source separation in humans is introduced, followed by estimation of μK (and the other kurtosis sources) in the healthy brain. Using this clinical CTI approach, we find that μK significantly contributes to total diffusional kurtosis both in gray and white matter tissue but, as expected, not in the ventricles. The first μK maps of the human brain are presented, revealing that the spatial distribution of μK provides a unique source of contrast, appearing different from isotropic and anisotropic kurtosis counterparts. Moreover, group average templates of these kurtosis sources have been generated for the first time, which corroborated our findings at the underlying individual-level maps. We further show that the common practice of ignoring μK and assuming the multiple gaussian component approximation for kurtosis source estimation introduces significant bias in the estimation of other kurtosis sources and, perhaps even worse, compromises their interpretation. Finally, a twofold acceleration of CTI is discussed in the context of potential future clinical applications. We conclude that CTI has much potential for future in vivo microstructural characterizations in healthy and pathological tissue.

Comparative Evaluation of Diffusion Kurtosis Imaging and Diffusion Tensor Imaging in Detecting Cerebral Microstructural Changes in Alzheimer Disease

OBJECTIVE

Comparative evaluation of diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) using a whole-brain atlas to comprehensively evaluate microstructural changes in the brain of Alzheimer disease (AzD) patients.

METHODS

Twenty-seven AzD patients and 25 age-matched controls were included. MRI data was analyzed using a whole-brain atlas with inclusion of 98 region of interests. White matter (WM) microstructural changes were assessed by Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), Kurtosis fractional anisotropy (KFA), mean kurtosis (MK), axial kurtosis (AK) and radial kurtosis (RK). Gray matter (GM) integrity was evaluated using KFA, MK, RK, AK and MD. Comparison of the DKI and DTI metrics were done using student t-test (p ≤ 0.001).

RESULTS

In AzD patients widespread increase in MD, AD and RD were found in various WM and GM region of interests. The extent of abnormality for DKI parameters was more limited in both GM and WM regions and revealed reduced kurtosis values except in lentiform nuclei. Both DKI and DTI parameters were sensitive to detect abnormality in WM areas with coherent and complex fiber arrangement. Receiver operating characteristic curve analysis for hippocampal values revealed the highest specificity of 88% for AK <0.6965 and highest sensitivity of 95.2% for MD >1.2659.

CONCLUSION

AzD patients have microstructural changes in both WM and GM and are well-depicted by both DKI and DTI. The alterations in kurtosis parameters, however, are more limited and correlate with areas in the brain primarily involved in cognition.

A Joint Framework for Denoising and Estimating Diffusion Kurtosis Tensors Using Multiple Prior Information

Diffusion kurtosis imaging (DKI) has been shown to be valuable in a wide range of neuroscientific and clinical applications. However, reliable estimation of DKI tensors is often compromised by noise, especially for the kurtosis tensor (KT). Here, we propose a joint denoising and estimating framework that integrates multiple sources of prior information, including nonlocal structural self-similarity (NSS), local spatial smoothness (LSS), physical relevance (PR) of the DKI model, and noise characteristics of magnitude diffusion MRI (dMRI) images for improved estimation of DKI tensors. The local and nonlocal spatial smoothing constraints are complementary to each other, making the proposed framework highly effective in reducing the noise fluctuations on DKI tensors, especially KT. As an additional refinement, we propose to impose a physically relevant constraint within our joint denoising and estimation framework. We further adopt the first-moment noise-corrected fitting model (M¹NCM) to remove the noncentral χ -distribution noise bias. The effectiveness of integrating multiple sources of priors into the joint framework is verified by comparing the proposed M¹NCM-NSS-LSS-PR method with various versions of M¹NCM-based estimators and two state-of-the-art methods. Results show that the proposed method outperformed the compared methods in simulations and in-vivo dMRI datasets of both spatially stationary and nonstationary noise distributions. The in-vivo experiments also show that the proposed M¹NCM-NSS-LSS-PR method was robust to the number of diffusion directions.

Machine learning applications on neuroimaging for diagnosis and prognosis of epilepsy: A review

Machine learning is playing an increasingly important role in medical image analysis, spawning new advances in the clinical application of neuroimaging. There have been some reviews on machine learning and epilepsy before, and they mainly focused on electrophysiological signals such as electroencephalography (EEG) and stereo electroencephalography (SEEG), while neglecting the potential of neuroimaging in epilepsy research. Neuroimaging has its important advantages in confirming the range of the epileptic region, which is essential in presurgical evaluation and assessment after surgery. However, it is difficult for EEG to locate the accurate epilepsy lesion region in the brain. In this review, we emphasize the interaction between neuroimaging and machine learning in the context of epilepsy diagnosis and prognosis. We start with an overview of epilepsy and typical neuroimaging modalities used in epilepsy clinics, MRI, DWI, fMRI, and PET. Then, we elaborate two approaches in applying machine learning methods to neuroimaging data: (i) the conventional machine learning approach combining manual feature engineering and classifiers, (ii) the deep learning approach, such as the convolutional neural networks and autoencoders. Subsequently, the application of machine learning on epilepsy neuroimaging, such as segmentation, localization, and lateralization tasks, as well as tasks directly related to diagnosis and prognosis are looked into in detail. Finally, we discuss the current achievements, challenges, and potential future directions in this field, hoping to pave the way for computer-aided diagnosis and prognosis of epilepsy.

Comparison of the pre-treatment functional MRI metrics' efficacy in predicting Locoregionally advanced nasopharyngeal carcinoma response to induction chemotherapy

Background

Functional MRI (fMRI) parameters analysis has been proven to be a promising tool of predicting therapeutic response to induction chemotherapy (IC) in nasopharyngeal carcinoma (NPC). The study was designed to identify and compare the value of fMRI parameters in predicting early response to IC in patients with NPC.

Methods

This prospective study enrolled fifty-six consecutively NPC patients treated with IC from January 2021 to May 2021. Conventional diffusion weighted imaging (DWI), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) protocols were performed before and after IC. Parameters maps (ADC, MD, MK, D_slow, D_fast, PF, K^trans, V_e and K_ep) of the primary tumor were calculated by the Functool post-processing software. The participants were classified as responding group (RG) and non-responding group (NRG) according to Response Evaluation Criteria in Solid Tumors 1.1. The fMRI parameters were compared before and after IC and between RG with NRG. Logistic regression analysis and ROC were performed to further identify and compare the efficacy of the parameters.

Results

After IC, the mean values of ADC(p < 0.001), MD(p < 0.001), D_slow(p = 0.001), PF(p = 0.030) and V_e(p = 0.003) significantly increased, while MK(p < 0.001), D_fast(p = 0.009) and K_ep(p = 0.003) values decreased dramatically, while no significant difference was detected in K^trans(p = 0.130). Compared with NRG, ADC-pre(p < 0.001), MD-pre(p < 0.001) and D_slow-pre(p = 0.002) values in RG were lower, while MK-pre(p = 0.017) values were higher. The areas under the ROC curves for the ADC-pre, MD-pre, MK-pre, D_slow-pre and PRE were 0.885, 0.855, 0.809, 0.742 and 0.912, with the optimal cutoff value of 1210 × 10^− 6 mm²/s, 1010 × 10^− 6 mm²/s, 832 × 10^− 6, 835 × 10^− 6 mm²/s and 0.799 respectively.

Conclusions

The pretreatment conventional DWI (ADC), DKI (MD and MK), and IVIM (D_slow) values derived from fMRI showed a promising potential in predicting the response of the primary tumor to IC in NPC patients.

Trial registration

This study was approved by ethics board of the Chinese PLA General Hospital, and registered on January 30, 2021, in Chinese Clinical Trial Registry (ChiCTR2100042863).

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-021-00428-0.

White matter microstructural impairments in amyotrophic lateral sclerosis: A mean apparent propagator MRI study

Background

White matter (WM) impairment is a hallmark of amyotrophic lateral sclerosis (ALS). This study evaluated the capacity of mean apparent propagator magnetic resonance imaging (MAP-MRI) for detecting ALS-related WM alterations.

Methods

Diffusion images were obtained from 52 ALS patients and 51 controls. MAP-derived indices [return-to-origin/-axis/-plane probability (RTOP/RTAP/RTPP) and non-Gaussianity (NG)/perpendicular/parallel NG (NG_⊥/NG_||)] were computed. Measures from diffusion tensor/kurtosis imaging (DTI/DKI) and neurite orientation dispersion and density imaging (NODDI) were also obtained. Voxel-wise analysis (VBA) was performed to determine differences in these parameters. Relationship between MAP parameters and disease severity (assessed by the revised ALS Functional Rating Scale (ALSFRS-R)) was evaluated by Pearson’s correlation analysis in a voxel-wise way. ALS patients were further divided into two subgroups: 29 with limb-only involvement and 23 with both bulbar and limb involvement. Subgroup analysis was then conducted to investigate diffusion parameter differences related to bulbar impairment.

Results

The VBA (with threshold of P < 0.05 after family-wise error correction (FWE)) showed that ALS patients had significantly decreased RTOP/RTAP/RTPP and NG/ NG_⊥/NG_|| in a set of WM areas, including the bilateral precentral gyrus, corona radiata, posterior limb of internal capsule, midbrain, middle corpus callosum, anterior corpus callosum, parahippocampal gyrus, and medulla. MAP-MRI had the capacity to capture WM damage in ALS, which was higher than DTI and similar to DKI/NODDI. RTOP/RTAP/NG/NG_⊥/NG_|| parameters, especially in the bilateral posterior limb of internal capsule and middle corpus callosum, were significantly correlated with ALSFRS-R (with threshold of FWE-corrected P < 0.05). The VBA (with FWE-corrected P < 0.05) revealed the significant RTAP reduction in subgroup with both bulbar and limb involvement, compared with those with limb-only involvement.

Conclusions

Microstructural impairments in corticospinal tract and corpus callosum represent the consistent characteristic of ALS. MAP-MRI could provide alternative measures depicting ALS-related WM alterations, complementary to the common diffusion imaging methods.

Diffusion kurtosis imaging detects the time-dependent progress of pathological changes in the oral rotenone mouse model of Parkinson's disease

Clinical diagnosis of Parkinson's disease (PD) occurs typically when a substantial proportion of dopaminergic neurons in the substantia nigra (SN) already died, and the first motor symptoms appear. Therefore, tools enabling the early diagnosis of PD are essential to identify early-stage PD patients in which neuroprotective treatments could have a significant impact. Here, we test the utility and sensitivity of the diffusion kurtosis imaging (DKI) in detecting progressive microstructural changes in several brain regions of mice exposed to chronic intragastric administration of rotenone, a mouse model that mimics the spatiotemporal progression of PD-like pathology from the ENS to the SN as described by Braak's staging. Our results show that DKI, especially kurtosis, can detect the progression of pathology-associated changes throughout the CNS. Increases in mean kurtosis were first observed in the dorsal motor nucleus of the vagus (DMV) after 2 months of exposure to rotenone and before the loss of dopaminergic neurons in the SN occurred. Remarkably, we also show that limited exposure to rotenone for 2 months is enough to trigger the progression of the disease in the absence of the environmental toxin, thus suggesting that once the first pathological changes in one region appear, they can self-perpetuate and progress within the CNS. Overall, our results show that DKI can be a useful radiological marker for the early detection and monitoring of PD pathology progression in patients with the potential to improve the clinical diagnosis and the development of neuroprotective treatments.

Progressive microstructural alterations in subcortical nuclei in Parkinson's disease: A diffusion magnetic resonance imaging study

OBJECTIVES

To explore the microstructural alterations in subcortical nuclei in Parkinson's disease (PD) at different stages with diffusion kurtosis imaging (DKI) and tensor imaging and to test the performance of diffusion metrics in identifying PD.

METHODS

108 PD patients (64 patients in early-stage PD group (EPD) and 44 patients in moderate-late-stage PD group (MLPD)) and 64 healthy controls (HC) were included. Tensor and kurtosis metrics in the subcortical nuclei were compared. Partial correlation was used to correlate the diffusion metrics and Unified Parkinson's Disease Rating Scale part-III (UPDRS-III) score. Logistic regression and receiver operating characteristic analysis were applied to test the diagnostic performance of the diffusion metrics.

RESULTS

Compared with HC, both EPD and MLPD patients showed higher fractional anisotropy and axial diffusivity, lower mean kurtosis (MK) and axial kurtosis in substantia nigra, lower MK and radial kurtosis (RK) in globus pallidus (GP) and thalamus (all p < 0.05). Compared with EPD, MLPD patients showed lower MK and RK in GP and thalamus (all p < 0.05). MK and RK in GP and thalamus were negatively correlated with UPDRS-III score (all p < 0.01). The logistic regression model combining kurtosis and tensor metrics showed the best performance in diagnosing PD, EPD, and MLPD (areas under curve were 0.817, 0.769, and 0.914, respectively).

CONCLUSIONS

PD has progressive microstructural alterations in the subcortical nuclei. DKI is sensitive to detect microstructural alterations in GP and thalamus during PD progression. Combining kurtosis and tensor metrics can achieve a good performance in diagnosing PD.

Diffusion Magnetic Resonance Imaging-Based Biomarkers for Neurodegenerative Diseases

There has been an increasing prevalence of neurodegenerative diseases with the rapid increase in aging societies worldwide. Biomarkers that can be used to detect pathological changes before the development of severe neuronal loss and consequently facilitate early intervention with disease-modifying therapeutic modalities are therefore urgently needed. Diffusion magnetic resonance imaging (MRI) is a promising tool that can be used to infer microstructural characteristics of the brain, such as microstructural integrity and complexity, as well as axonal density, order, and myelination, through the utilization of water molecules that are diffused within the tissue, with displacement at the micron scale. Diffusion tensor imaging is the most commonly used diffusion MRI technique to assess the pathophysiology of neurodegenerative diseases. However, diffusion tensor imaging has several limitations, and new technologies, including neurite orientation dispersion and density imaging, diffusion kurtosis imaging, and free-water imaging, have been recently developed as approaches to overcome these constraints. This review provides an overview of these technologies and their potential as biomarkers for the early diagnosis and disease progression of major neurodegenerative diseases.

Microstructural changes in the cingulate gyrus of patients with mild cognitive impairment induced by cerebral small vessel disease

Objective: The purpose of our study was to distinguish the changes in the microstructure of the cingulate cortex in patients with mild cognitive impairment (MCI) induced by cerebral small vessel disease (CSVD).Method: 80 patients were diagnosed with CSVD in this study, including 55 patients with MCI and 25 patients without MCI. Diffusion kurtosis imaging (DKI) and Montreal cognitive assessment (MoCA) were performed in all patients. The anterior cingulate gyrus, posterior cingulate gyrus and middle cingulate gyrus were selected as the regions of interest, and some parameters were recorded.Results: Compared with the non-MCI group, the MCI group mainly showed obviously higher mean diffusion (MD) and radial diffusion (RD) values (P = 0.022 and P = 0.029) but lower fractional anisotropy (FA), axial kurtosis (AK), mean kurtosis (MK) and radial kurtosis (RK) values (P = 0.047, P = 0.001, P < 0.01, and P = 0.001, respectively) in the right anterior cingulate gyrus. Meanwhile, in the right posterior cingulate gyrus, the MCI group also showed higher axial diffusion (AD) and MD (P = 0.027 and P = 0.030) and lower AK (P = 0.014). Additionally, negative correlations of AD, MD, and RD with MoCA scores and positive correlations of FA, AK, MK and RK with MoCA scores were observed in some regions of the cingulate gyrus.Conclusions: DKI is a good method to examine microstructural damage in the cingulate cortex, and some parameters of DKI may be used as imaging biomarkers to detect early MCI in patients with CSVD.

The effect of magnetic guiding BMSCs on hypoxic-ischemic brain damage via magnetic resonance imaging evaluation

Hypoxic-ischemic brain damage (HIBD) is a critical disease in pediatric neurosurgery with high mortality rate and frequently leads to neurological sequelae. The role of bone marrow mesenchymal stem cells (BMSCs) in neuroprotection has been recognized. However, using the imaging methods to dynamically assess the neuroprotective effects of BMSCs is rarely reported. In this study, BMSCs were isolated, cultured and identified. Flow cytometry assay had shown the specific surface molecular markers of BMSCs, which indicated that the cultivated cells were purified BMSCs. The results demonstrated that CD29 and CD90 were highly expressed, whilst CD45 and CD11b were negatively expressed. Further, BMSCs were transplanted into Sprague Dawley (SD) rats established HIBD via three ways, including lateral ventricle (LV) injection, tail vein (TV) injection, and LV injection with magnetic guiding. Magnetic resonance imaging (MRI) was used to monitor and assess the treatment effect of super paramagnetic iron oxide (SPIO)-labeled BMSCs. The mean kurtosis (MK) values from diffusion kurtosis imaging (DKI) exhibited the significant differences. It was found that the MK value of HIBD group increased compared with that in Sham. At the meantime, the MK values of LV + HIBD, TV + HIBD and Magnetic+LV + HIBD groups decreased compared with that in HIBD group. Among these, the MK value reduced most significantly in Magnetic+LV + HIBD group. MRI illustrated that the treatment effect of Magnetic+LV + HIBD group was best. In addition, HE staining and TUNEL assay measured the pathological changes and apoptosis of brain tissues, which further verified the MRI results. All data suggest that magnetic guiding BMSCs, a targeted delivery way, is a new strategic theory for HIBD treatment. The DKI technology of MRI can dynamically evaluate the neuroprotective effects of transplanted BMSCs in HIBD.

The association between White matter microstructure alterations detected by Diffusional kurtosis imaging in Neural circuit and post-stroke depression

AIM

In order to study the mechanism of post-stroke depression (PSD), we used diffusion kurtosis imaging (DKI) to describe the changes in white matter (WM) microstructure in PSD patients, to investigate the association between WM damage in limbic-cortical-striatal-pallidal-thalamic (LCSPT) circuit and PSD, and the utility of DKI in the diagnosis of PSD.

METHODS

Fifty-eight participants were divided into different groups: control group (n=20), stroke patients without depression (Non-PSD, n=21) and PSD group (n=17). All were taken DKI scans. The WM of bilateral superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, temporal lobe, parietal lobe, occipital lobe, the anterior and posterior limb of internal capsule, the genu and splenium of corpus callosum were selected as the regions of interest (ROI) and selected mean kurtosis (MK), radial kurtosis (RK), axial kurtosis (AK) as the DKI parameters.

RESULTS

Compared with control and Non-PSD, MK of PSD group in bilateral superior frontal gyrus, middle frontal gyrus, temporal lobe and the genu of corpus callosum were decreased significantly, as well as the RK in left superior frontal gyrus, bilateral middle frontal gyrus and temporal lobe. But there was no significant difference in AK. Besides, the decrease in MK and RK in frontal and temporal lobe was negatively associated with the severity of PSD.

CONCLUSION

Our research indicated that the damage to WM microstructure in the frontal lobe, temporal lobe and the genu of corpus callosum may be related toPSD. DKI explores the microstructural changes of WM in PSD patients and may be an auxiliary diagnostic method for PSD.

Diffusion kurtosis imaging to evaluate the effect and mechanism of tetramethylpyrazine on cognitive impairment induced by lipopolysaccharide in rats

Using diffusion kurtosis imaging (DKI) to evaluate the brain changes, the therapeutic effect and mechanism of tetramethylpyrazine in rats with dementia induced by lipopolysaccharide. Thirty-six male Sprague-Dawley rats were randomly divided into control group and five groups pretreated with sham operation, lipopolysaccharide(150ug) and three doses of tetramethylpyrazine(5, 10, and 20 mg/mL respectively). The Morris water maze test was used to evaluate cognitive ability. DKI and histology were performed. Low-dose of tetramethylpyrazine pretreated rats showed lower escape latency(6th day: 15.92seconds(s) vs. 5.11 s, P = 0.001), spent more time in the target quadrant(15.67 s vs. 29.83 s, P = 0.009) and crossed the platform area more frequently(3.50 vs. 9.17, P = 0.001) than rats in the LPS-treated group. Compared to sham group, the fractional anisotropy (FA), axial diffusion (Da), mean kurtosis (MK), and axial kurtosis (Ka) values in the cortex of lipopolysaccharide group were lower (P = 0.021,0.003,0.003,0.001,respectively).The MK, Ka, Kr, and FA values in the hippocampus of the lipopolysaccharide group were higher (P = 0.01, 0.026,0.007,0.003,respectively),while MD and Da values were lower (P = 0.045,0.044, respectively). Tetramethylpyrazine-pretreated rats showed higher values of FA, MD, Da, MK, and Ka in the cortex, lower MK, Ka, Kr, and FA values and higher MD,Da values in the hippocampus than the lipopolysaccharide group. Histologically, prominent inflammatory cells infiltration in the brain parenchyma of lipopolysaccharide group were observed, while groups pretreated using tetramethylpyrazine were alleviated. Tetramethylpyrazine can improve cognitive dysfunction induced by lipopolysaccharide. DKI can sensitively detect microstructure integrity of brain parenchyma in a non-invasive manner.

Elucidating the complex organization of neural micro-domains in the locust Schistocerca gregaria using dMRI

To understand brain function it is necessary to characterize both the underlying structural connectivity between neurons and the physiological integrity of these connections. Previous research exploring insect brain connectivity has typically used electron microscopy techniques, but this methodology cannot be applied to living animals and so cannot be used to understand dynamic physiological processes. The relatively large brain of the desert locust, Schistercera gregaria (Forksȧl) is ideal for exploring a novel methodology; micro diffusion magnetic resonance imaging (micro-dMRI) for the characterization of neuronal connectivity in an insect brain. The diffusion-weighted imaging (DWI) data were acquired on a preclinical system using a customised multi-shell diffusion MRI scheme optimized to image the locust brain. Endogenous imaging contrasts from the averaged DWIs and Diffusion Kurtosis Imaging (DKI) scheme were applied to classify various anatomical features and diffusion patterns in neuropils, respectively. The application of micro-dMRI modelling to the locust brain provides a novel means of identifying anatomical regions and inferring connectivity of large tracts in an insect brain. Furthermore, quantitative imaging indices derived from the kurtosis model that include fractional anisotropy (FA), mean diffusivity (MD) and kurtosis anisotropy (KA) can be extracted. These metrics could, in future, be used to quantify longitudinal structural changes in the nervous system of the locust brain that occur due to environmental stressors or ageing.

Diffusion kurtosis imaging detects subclinical white matter abnormalities in Phenylketonuria

OBJECTIVE

Phenylketonuria (PKU) is an autosomal recessive disorder whereby deficiencies in phenylalanine metabolism cause progressive neurological dysfunction. Managing PKU is challenging, with disease monitoring focussed on short-term phenylalanine control rather than measures of neuronal damage. Conventional imaging lacks sensitivity, however diffusion kurtosis imaging (DKI), a new MRI method may reveal subclinical white matter structural changes in PKU.

METHODS

This cohort study involved adults with PKU recruited during routine clinical care. MRI, neurocognitive assessment and historical phenylalanine (Phe) levels were collected. A hypothesis-generating case study comparing diet-compliant and non-compliant siblings confirmed that DKI metrics are sensitive to dietary adherence and prompted a candidate metric (K_rad/K_FA ratio). We then tested this metric in a Replication cohort (PKU = 20; controls = 43).

RESULTS

Both siblings scored outside the range of controls for all DKI-based metrics, with severe changes in the periventricular white matter and a gradient of severity toward the cortex. K_rad/K_FA provided clear separation by diagnosis in the Replication cohort (p < 0.001 in periventricular, deep and pericortical compartments). The ratio also correlated negatively with attention (r = -0.51 & -0.50, p < 0.05) and positively with 3-year mean Phe (r = 0.45 & 0.58, p < 0.01).

CONCLUSION

DKI reveals regionally-specific, progressive abnormalities of brain diffusion characteristics in PKU, even in the absence of conspicuous clinical signs or abnormalities on conventional MRI. A DKI-based marker derived from these scores (K_rad/K_FA ratio) was sensitive to cognitive impairment and PKU control over the medium term and may provide a meaningful subclinical biomarker of end-organ damage.

Gray and white matter abnormalities in primary trigeminal neuralgia with and without neurovascular compression

PURPOSE

Previous researches have reported gray and white matter microalterations in primary trigeminal neuralgia (TN) with neurovascular compression (NVC). The central mechanism underlying TN without NVC are unknown but may include changes in specific brain regions or circuitries. This study aimed to investigate abnormalities in the gray matter (GM) and white matter (WM) of the whole brain and the possible pathogenetic mechanism underlying this disease.

METHODS

We analyzed brain morphologic images of TN patients, 23 with NVC (TN wNVC) and 22 without NVC (TN wNVC) compared with 45 healthy controls (HC). All subjects underwent 3T-magnetic resonance imaging and pain scale evaluation. Voxel-based morphometry (VBM) and surface-based morphometry (SBM) were used to investigate whole brain grey matter quantitatively; graph theory was applied to obtain network measures based on extracted DTI data based on DTI data of the whole brains. Sensory and affective pain rating indices were assessed using the visual analog scale (VAS) and short-form McGill Pain Questionnaire (SF-MPQ).

RESULTS

The VBM and SBM analyses revealed widespread decreases in GM volume and cortical thickness in TN wNVC compared to TN woNVC, and diffusion metrics measures and topology organization changes revealed DTI showed extensive WM integrity alterations. However, above structural changes differed between TN wNVC and TN woNVC, and were related to specific chronic pain modulation mechanism.

CONCLUSION

Abnormalities in characteristic regions of GM and WM structural network were found in TN woNVC compared with TN wNVC group, suggesting differences in pathophysiology of two types of TN.

[Met5]-enkephalin preserves diffusion metrics in EAE mice

Multiple sclerosis is a chronic progressive neurological disorder that has few distinctive biomarkers associated with disease progression or response to therapy. This research investigated whether non-invasive imaging correlated with animal behavior and morphological indicators of disease in response to serum levels of [Met⁵]-enkephalin. Using the experimental autoimmune encephalomyelitis (EAE) model, adult female C57BL/6 J mice were randomized to receive daily injections of 0.1 mg/kg naltrexone (NTX) (= low dose naltrexone, LDN), 10 mg/kg Opioid Growth Factor (OGF) (chemically termed [Met⁵]-enkephalin) or saline beginning at the time of disease induction. Daily composite behavior scores were recorded over a 30-day period based on tail tone, gait, righting reflex, and limb strength. Prior to disease onset (day 7), and at peak disease (day 18), mice were imaged and tissues (blood and spinal cord) collected at day 30 for serum analyses of OGF and morphology. Serum OGF levels of EAE mice treated with saline were significantly reduced from baseline and from normal mice. Longitudinal cohort data demonstrated an increase in fractional anisotropy in all cohorts by day 18. There was a significant decrease in radial diffusivity in the saline group seen at day 18 whereas the axial diffusivity was not altered amongst treatment groups. Treatment with OGF or LDN resulted in mean diffusivity rates that were comparable to baseline (normal) levels at days 7 and 18. Luxol fast blue staining of the lumbar spinal cords demonstrated a 16 % reduction in myelin staining in saline treated EAE animals when compared to OGF and LDN treated EAE mice. Immunohistochemistry with Olig2 (pan-oligodendrocyte marker) and myelin basic protein (MBP) revealed that OGF and LDN treatment restored the area (%) of MBP and number of oligodendrocytes to that of normal spinal cord (∼75 %). Saline treated EAE mice had more demyelination and fewer oligodendrocytes than normal mice. Collectively, these data suggest that a panel of biomarkers including imaging, serum biomarker levels, and behavior correlate with progression of disease, and may begin to validate use of specific non-invasive markers for MS.

Evaluating High Spatial Resolution Diffusion Kurtosis Imaging at 3T: Reproducibility and Quality of Fit

BACKGROUND

Diffusion kurtosis imaging (DKI) quantifies the non-Gaussian diffusion of water within tissue microstructure. However, it has increased fitting parameters and requires higher b-values. Evaluation of DKI reproducibility is important for clinical purposes.

PURPOSE

To assess the reproducibility in whole-brain high-resolution DKI at varying b-values.

STUDY TYPE

Retrospective.

SUBJECTS AND PHANTOMS

In all, 44 individuals from the test-retest Human Connectome Project (HCP) database and 12 3D-printed phantoms.

FIELD STRENGTH/SEQUENCE

Diffusion-weighted multiband echo-planar imaging sequence at 3T and 9.4T. magnetization-prepared rapid acquisition gradient echo at 3T for in vivo structural data only.

ASSESSMENT

From HCP data with b-values = 1000, 2000, 3000 s/mm² (dataset A), two additional datasets with b-values = 1000, 3000 s/mm² (dataset B) and b-values = 1000, 2000 s/mm² (dataset C) were extracted. Estimated DKI metrics from each dataset were used for evaluating reproducibility and fitting quality in white matter (WM) and gray matter (GM) based on whole-brain and regions of interest (ROIs).

STATISTICAL TESTS

DKI reproducibility was assessed using the within-subject coefficient of variation (CoV), fitting residuals to evaluate DKI fitting accuracy and Pearson's correlation to investigate the presence of systematic biases. Repeated measures analysis of variance was used for statistical comparison.

RESULTS

Datasets A and B exhibited lower DKI CoVs (<20%) compared to C (<50%) in both WM and GM ROIs (all P < 0.05). This effect varies between DKI and DTI parameters (P < 0.005). Whole-brain fitting residuals were consistent across datasets (P > 0.05), but lower residuals in dataset B were detected for the WM ROIs (P < 0.001). A similar trend was observed for the phantom data CoVs (<7.5%) at varying fiber orientations for datasets A and B. Finally, dataset C was characterized by higher residuals across the different fiber crossings (P < 0.05).

DATA CONCLUSION

The study demonstrates that high reproducibility can still be achieved within a reasonable scan time, specifically dataset B, supporting the potential of DKI for aiding clinical tools in detecting microstructural changes.

Diffusion kurtosis imaging and pathological comparison of early hypoxic-ischemic brain damage in newborn piglets

To investigate the application value of magnetic resonance diffusion kurtosis imaging (DKI) in hypoxic–ischemic brain damage (HIBD) in newborn piglets and to compare imaging and pathological results. Of 36 piglets investigated, 18 were in the experimental group and 18 in the control group. The HIBD model was established in newborn piglets by ligating the bilateral common carotid arteries and placing them into hypoxic chamber. All piglets underwent conventional MRI and DKI scans at 3, 6, 9, 12, 16, and 24 h postoperatively. Mean kurtosis (MK) and mean diffusivity (MD) maps were constructed. Then, the lesions were examined using light and electron microscopy and compared with DKI images. The MD value of the lesion area gradually decreased and the MK value gradually increased in the experimental group with time. The lesion areas gradually expanded with time; MK lesions were smaller than MD lesions. Light microscopy revealed neuronal swelling in the MK- and MD-matched and mismatched regions. Electron microscopy demonstrated obvious mitochondrial swelling and autophagosomes in the MK- and MD-matched region but normal mitochondrial morphology or mild swelling in the mismatched region. DKI can accurately evaluate early ischemic–hypoxic brain injury in newborn piglets.

Cerebral diffusion kurtosis imaging to assess the pathophysiology of postpartum depression

Postpartum depression (PPD), a main cause of maternal suicide, is an important issue in perinatal mental health. Recently, cerebral diffusion tensor imaging (DTI) studies have shown reduced fractional anisotropy (FA) in major depressive disorder (MDD) patients. There are, however, no reports using diffusion kurtosis imaging (DKI) for evaluation of PPD. This was a Japanese single-institutional prospective study from 2016 to 2019 to examine the pathophysiological changes in the brain of PPD patients using DKI. The DKI data from 3.0 T MRI of patients one month after delivery were analyzed; the patients were examined for PPD by a psychiatrist. The mean kurtosis (MK), FA and mean diffusivity (MD) were calculated from the DKI data and compared between PPD and non-PPD groups using tract-based spatial statistics analysis. Of the 75 patients analyzed, eight patients (10.7%) were diagnosed as having PPD. In the PPD group, FA values in the white matter and thalamus were significantly lower and MD values in the white matter and putamen were significantly higher. The area with significant differences in MD value was more extensive (40.8%) than the area with significant differences in FA value (6.5%). These findings may reflect pathophysiological differences of PPD compared with MDD.

Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

Early Stage Markers of Late Delayed Neurocognitive Decline Using Diffusion Kurtosis Imaging of Temporal Lobe in Nasopharyngeal Carcinoma Patients

Purpose: To determine whether the early assessment of temporal lobe microstructural changes using diffusion kurtosis imaging (DKI) can predict late delayed neurocognitive decline after radiotherapy in nasopharyngeal carcinoma (NPC) patients. Methods and Materials: Fifty-four NPC patients undergoing intensity-modulated radiotherapy (IMRT) participated in a prospective DKI magnetic resonance (MR) imaging study. MR imaging was acquired prior to IMRT (-0), 1 month (-1), and 3 (-3) months after IMRT. Kurtosis (Kmean, Kax, Krad) and Diffusivity (Dmean, Dax, Drad) variables in the temporal lobe gray and white matter were computed. Neurocognitive function tests (MoCA) were administered pre-radiotherapy and at 2 years post-IMRT follow-up. All the patients were divided into neurocognitive function decline (NFD group) and neurocognitive function non-decline groups (NFND group) according to whether the MoCA score declined ≥3 2 years after IMRT. All the DKI metrics were compared between the two groups, and the best imaging marker was chosen for predicting a late delayed neurocognitive decline. Results: Kurtosis (Kmean-1, Kmean-3, Kax-1, Kax-3, Krad-1, and Krad-3) and Diffusivity (Dmean-1 and Dmean-3) of white matter were significantly different between the two groups (p<0.05). Axial Kurtosis (Kax-1, Kax-3) of gray matter was significantly different between the two groups (p<0.05). By receiver operating characteristic (ROC) curves, Kmean-1 of white matter performed best in predicting of MoCA scores delayed decline (p<0.05). The radiation dose was also significantly different between NFD and NFND group (p=0.031). Conclusions: Temporal lobe white matter is more vulnerable to microstructural changes and injury following IMRT in NPC. Metrics derived from DKI should be considered as imaging markers for predicting a late delayed neurocognitive decline. Both temporal lobe white and gray matter show microstructural changes detectable by DKI. The Kmean early after radiotherapy has the best prediction performance.

Discriminating VCID subgroups: A diffusion MRI multi-model fusion approach

BACKGROUND

Vascular cognitive impairment and dementia (VCID) and Alzheimer's disease are predominant diseases among the aging population resulting in decline of various cognitive domains. Diffusion weighted MRI (DW-MRI) has been shown to be a promising aid in the diagnosis of such diseases. However, there are various models of DW-MRI and the interpretation of diffusion metrics depends on the model used in fitting data. Most previous studies are entirely based on parameters calculated from a single diffusion model.

NEW METHOD

We employ a data fusion framework wherein diffusion metrics from different models such as diffusion tensor imaging, diffusion kurtosis imaging and constrained spherical deconvolution model are fused using well known blind source separation approach to investigate white matter microstructural changes in population comprising of controls and VCID subgroups. Multiple comparisons between subject groups and prediction analysis using features from individual models and proposed fusion model are carried out to evaluate performance of proposed method.

RESULTS

Diffusion features from individual models successfully distinguished between controls and disease groups, but failed to differentiate between disease groups, whereas fusion approach showed group differences between disease groups too. WM tracts showing significant differences are superior longitudinal fasciculus, anterior thalamic radiation, arcuate fasciculus, optic radiation and corticospinal tract.

COMPARISON WITH EXISTING METHOD

ROC analysis showed increased AUC for fusion (AUC = 0.913, averaged across groups and tracts) compared to that of uni-model features (AUC = 0.77) demonstrating increased sensitivity of proposed method.

CONCLUSION

Overall our results highlight the benefits of multi-model fusion approach, providing improved sensitivity in discriminating VCID subgroups.

Advanced diffusion magnetic resonance imaging in patients with Alzheimer's and Parkinson's diseases

The prevalence of neurodegenerative diseases is increasing as human longevity increases. The objective biomarkers that enable the staging and early diagnosis of neurodegenerative diseases are eagerly anticipated. It has recently become possible to determine pathological changes in the brain without autopsy with the advancement of diffusion magnetic resonance imaging techniques. Diffusion magnetic resonance imaging is a robust tool used to evaluate brain microstructural complexity and integrity, axonal order, density, and myelination via the micron-scale displacement of water molecules diffusing in tissues. Diffusion tensor imaging, a type of diffusion magnetic resonance imaging technique is widely utilized in clinical and research settings; however, it has several limitations. To overcome these limitations, cutting-edge diffusion magnetic resonance imaging techniques, such as diffusional kurtosis imaging, neurite orientation dispersion and density imaging, and free water imaging, have been recently proposed and applied to evaluate the pathology of neurodegenerative diseases. This review focused on the main applications, findings, and future directions of advanced diffusion magnetic resonance imaging techniques in patients with Alzheimer’s and Parkinson’s diseases, the first and second most common neurodegenerative diseases, respectively.

A Systematic Review and Implementation of IoT-Based Pervasive Sensor-Enabled Tracking System for Dementia Patients

In today's world, 46.8 million people suffer from brain related diseases. Dementia is most prevalent of all. In general scenario, a dementia patient lacks proper guidance in searching out the way to return back at his/her home. Thus, increasing the risk of getting damaged at individual-health level. Therefore, it is important to track their movement in more sophisticated manner as possible. With emergence of wearables, GPS sensors and Internet of Things (IoT), such devices have become available in public domain. Smartphone apps support caregiver to locate the dementia patients in real-time. RF, GSM, 3G, Wi-Fi and 4G technology fill the communication gap between patient and caregiver to bring them closer. In this paper, we incorporated 7 most popular wearables for investigation to seek appropriateness for dementia tracking in recent times in systematic manners. We performed an in-depth review of these wearables as per the cost, technology wise and application wise characteristics. A case novel study i.e. IoT-based Force Sensor Resistance enabled System-FSRIoT, has been proposed and implemented to validate the effectiveness of IoT in the domain of smarter dementia patient tracking in wearable form factor. The results show promising aspect of a whole new notion to leverage efficient assistive physio-medical healthcare to the dementia patients and the affected family members to reduce life risks and achieve a better social life.