A Comparative Atlas-Based Recognition of Mild Cognitive Impairment With Voxel-Based Morphometry

Identifying discriminative features of brain network for prediction of Alzheimer's disease using graph theory and machine learning

Alzheimer's disease (AD) is a challenging neurodegenerative condition, necessitating early diagnosis and intervention. This research leverages machine learning (ML) and graph theory metrics, derived from resting-state functional magnetic resonance imaging (rs-fMRI) data to predict AD. Using Southwest University Adult Lifespan Dataset (SALD, age 21-76 years) and the Open Access Series of Imaging Studies (OASIS, age 64-95 years) dataset, containing 112 participants, various ML models were developed for the purpose of AD prediction. The study identifies key features for a comprehensive understanding of brain network topology and functional connectivity in AD. Through a 5-fold cross-validation, all models demonstrate substantial predictive capabilities (accuracy in 82-92% range), with the support vector machine model standing out as the best having an accuracy of 92%. Present study suggests that top 13 regions, identified based on most important discriminating features, have lost significant connections with thalamus. The functional connection strengths were consistently declined for substantia nigra, pars reticulata, substantia nigra, pars compacta, and nucleus accumbens among AD subjects as compared to healthy adults and aging individuals. The present finding corroborate with the earlier studies, employing various neuroimagining techniques. This research signifies the translational potential of a comprehensive approach integrating ML, graph theory and rs-fMRI analysis in AD prediction, offering potential biomarker for more accurate diagnostics and early prediction of AD.

Gray matter atrophy and white matter lesions burden in delayed cognitive decline following carbon monoxide poisoning

Gray matter (GM) atrophy and white matter (WM) lesions may contribute to cognitive decline in patients with delayed neurological sequelae (DNS) after carbon monoxide (CO) poisoning. However, there is currently a lack of evidence supporting this relationship. This study aimed to investigate the volume of GM, cortical thickness, and burden of WM lesions in 33 DNS patients with dementia, 24 DNS patients with mild cognitive impairment, and 51 healthy controls. Various methods, including voxel-based, deformation-based, surface-based, and atlas-based analyses, were used to examine GM structures. Furthermore, we explored the connection between GM volume changes, WM lesions burden, and cognitive decline. Compared to the healthy controls, both patient groups exhibited widespread GM atrophy in the cerebral cortices (for volume and cortical thickness), subcortical nuclei (for volume), and cerebellum (for volume) (p < .05 corrected for false discovery rate [FDR]). The total volume of GM atrophy in 31 subregions, which included the default mode network (DMN), visual network (VN), and cerebellar network (CN) (p < .05, FDR-corrected), independently contributed to the severity of cognitive impairment (p < .05). Additionally, WM lesions impacted cognitive decline through both direct and indirect effects, with the latter mediated by volume reduction in 16 subregions of cognitive networks (p < .05). These preliminary findings suggested that both GM atrophy and WM lesions were involved in cognitive decline in DNS patients following CO poisoning. Moreover, the reduction in the volume of DMN, VN, and posterior CN nodes mediated the WM lesions-induced cognitive decline.

Characterizing the Visual Cortex Function in Cognitive Task-Induced Mental Load: An Functional Magnetic Resonance Imaging Study

Introduction: The mental load caused by simultaneous multitasking can affect visual information processing and reduce its ability. This study investigated the effect of mental load caused by cognitive tasks simultaneously with visual task on the number of active voxels in the visual cortex. Methods: This study recruited 22 individuals with a mean age of 24.72 ± 5.47 years. 3-Tesla functional magnetic resonance imaging (fMRI) was used to examine the functions of the visual cortex and amygdala region during three different task conditions: visual task alone, visual task with an auditory n-back task, and visual task with an arithmetic task. The visual stimuli consisted of Gabor patches with a contrast of 55% at spatial frequencies of 0.25, 4, and 9 cycles per degree (cpd). These were presented in three trials of eight blocks with a stimulation time of 12 sec and a rest time of 14 sec. Results: Activated brain voxels in the primary, secondary, and associated visual cortex areas were reduced in response to the mental load imposed by the n-back and arithmetic tasks. This reduction was greater for a spatial frequency of 0.25 cpd in the n-back task condition and spatial frequency of 9 cpd in the arithmetic task condition. In addition, the amygdala was stimulated in 2-back task and arithmetic task conditions. Conclusions: This study revealed a decline in the number of activated voxels of the visual cortex due to the mental load caused by simultaneous cognitive tasks, confirming the findings of previous psychophysical studies.

A radiomics-based brain network in T1 images: construction, attributes, and applications

T1 image is a widely collected imaging sequence in various neuroimaging datasets, but it is rarely used to construct an individual-level brain network. In this study, a novel individualized radiomics-based structural similarity network was proposed from T1 images. In detail, it used voxel-based morphometry to obtain the preprocessed gray matter images, and radiomic features were then extracted on each region of interest in Brainnetome atlas, and an individualized radiomics-based structural similarity network was finally built using the correlational values of radiomic features between any pair of regions of interest. After that, the network characteristics of individualized radiomics-based structural similarity network were assessed, including graph theory attributes, test-retest reliability, and individual identification ability (fingerprinting). At last, two representative applications for individualized radiomics-based structural similarity network, namely mild cognitive impairment subtype discrimination and fluid intelligence prediction, were exemplified and compared with some other networks on large open-source datasets. The results revealed that the individualized radiomics-based structural similarity network displays remarkable network characteristics and exhibits advantageous performances in mild cognitive impairment subtype discrimination and fluid intelligence prediction. In summary, the individualized radiomics-based structural similarity network provides a distinctive, reliable, and informative individualized structural brain network, which can be combined with other networks such as resting-state functional connectivity for various phenotypic and clinical applications.

Cognition's dependence on functional network integrity with age is conditional on structural network integrity

Maintaining good cognitive function is crucial for well-being across the lifespan. We proposed that the degree of cognitive maintenance is determined by the functional interactions within and between large-scale brain networks. Such connectivity can be represented by the white matter architecture of structural brain networks that shape intrinsic neuronal activity into integrated and distributed functional networks. We explored how the function-structure connectivity convergence, and the divergence of functional connectivity from structural connectivity, contribute to the maintenance of cognitive function across the adult lifespan. Multivariate analyses were used to investigate the relationship between function-structure connectivity convergence and divergence with multivariate cognitive profiles, respectively. Cognitive function was increasingly dependent on function-structure connectivity convergence as age increased. The dependency of cognitive function on connectivity was particularly strong for high-order cortical networks and subcortical networks. The results suggest that brain functional network integrity sustains cognitive functions in old age, as a function of the integrity of the brain's structural connectivity.

Identifying Alzheimer's disease and mild cognitive impairment with atlas-based multi-modal metrics

Introduction

Multi-modal neuroimaging metrics in combination with advanced machine learning techniques have attracted more and more attention for an effective multi-class identification of Alzheimer's disease (AD), mild cognitive impairment (MCI) and health controls (HC) recently.

Methods

In this paper, a total of 180 subjects consisting of 44 AD, 66 MCI and 58 HC subjects were enrolled, and the multi-modalities of the resting-state functional magnetic resonance imaging (rs-fMRI) and the structural MRI (sMRI) for all participants were obtained. Then, four kinds of metrics including the Hurst exponent (HE) metric and bilateral hippocampus seed independently based connectivity metrics generated from fMRI data, and the gray matter volume (GMV) metric obtained from sMRI data, were calculated and extracted in each region of interest (ROI) based on a newly proposed automated anatomical Labeling (AAL3) atlas after data pre-processing. Next, these metrics were selected with a minimal redundancy maximal relevance (MRMR) method and a sequential feature collection (SFC) algorithm, and only a subset of optimal features were retained after this step. Finally, the support vector machine (SVM) based classification methods and artificial neural network (ANN) algorithm were utilized to identify the multi-class of AD, MCI and HC subjects in single modal and multi-modal metrics respectively, and a nested ten-fold cross-validation was utilized to estimate the final classification performance.

Results

The results of the SVM and ANN based methods indicated the best accuracies of 80.36 and 74.40%, respectively, by utilizing all the multi-modal metrics, and the optimal accuracies for AD, MCI and HC were 79.55, 78.79 and 82.76%, respectively, in the SVM based method. In contrast, when using single modal metric, the SVM based method obtained a best accuracy of 72.62% with the HE metric, and the accuracies for AD, MCI and HC subjects were just 56.82, 80.30 and 75.86%, respectively. Moreover, the overlapping abnormal brain regions detected by multi-modal metrics were mainly located at posterior cingulate gyrus, superior frontal gyrus and cuneus.

Conclusion

Taken together, the SVM based method with multi-modal metrics could provide effective diagnostic information for identifying AD, MCI and HC subjects.

Predicting mild cognitive impairment progression to Alzheimer's disease based on machine learning analysis of cortical morphological features

PURPOSE

To establish a model for predicting mild cognitive impairment (MCI) progression to Alzheimer's disease (AD) using morphological features extracted from a joint analysis of voxel-based morphometry (VBM) and surface-based morphometry (SBM).

METHODS

We analyzed data from 121 MCI patients from the Alzheimer's Disease Neuroimaging Initiative, 32 of whom progressed to AD during a 4-year follow-up period and were classified as the progression group, while the remaining 89 were classified as the non-progression group. Patients were divided into a training set (n = 84) and a testing set (n = 37). Morphological features measured by VBM and SBM were extracted from the cortex of the training set and dimensionally reduced to construct morphological biomarkers using machine learning methods, which were combined with clinical data to build a multimodal combinatorial model. The model's performance was evaluated using receiver operating characteristic curves on the testing set.

RESULTS

The Alzheimer's Disease Assessment Scale (ADAS) score, apolipoprotein E (APOE4), and morphological biomarkers were independent predictors of MCI progression to AD. The combinatorial model based on the independent predictors had an area under the curve (AUC) of 0.866 in the training set and 0.828 in the testing set, with sensitivities of 0.773 and 0.900 and specificities of 0.903 and 0.747, respectively. The number of MCI patients classified as high-risk for progression to AD was significantly different from those classified as low-risk in the training set, testing set, and entire dataset, according to the combinatorial model (P < 0.05).

CONCLUSION

The combinatorial model based on cortical morphological features can identify high-risk MCI patients likely to progress to AD, potentially providing an effective tool for clinical screening.

Changes in Volume of Subregions Within Basal Ganglia in Obsessive–Compulsive Disorder: A Study With Atlas-Based and VBM Methods

Background

The role of basal ganglia in the pathogenesis of obsessive–compulsive disorder (OCD) remains unclear. The studies on volume changes of basal ganglia in OCD commonly use the VBM method; however, the Atlas-based method used in such research has not been reported. Atlas-based method has a lower false positive rate compared with VBM method, thus having advantages partly.

Objectives

The current study aimed to detect the volume changes of subregions within basal ganglia in OCD using Atlas-based method to further delineate the precise neural circuitry of OCD. What is more, we explored the influence of software used in Atlas-based method on the volumetric analysis of basal ganglia and compared the results of Atlas-based method and regularly used VBM method.

Methods

We analyzed the brain structure images of 37 patients with OCD and 41 healthy controls (HCs) using the VBM method, Atlas-based method based on SPM software, or Freesurfer software to find the areas with significant volumetric variation between the two groups, and calculated the effects size of these areas.

Results

VBM analysis revealed a significantly increased volume of bilateral lenticular nucleus in patients compared to HCs. In contrast, Atlas-based method based on Freesurfer revealed significantly increased volume of left globus pallidus in patients, and the largest effect size of volumetric variation was revealed by Freesurfer analysis.

Conclusions

This study showed that the volume of bilateral lenticular nucleus significantly increased in patients compared to HCs, especially left globus pallidus, which was in accordance with the previous findings. In addition, Freesurfer is better than SPM and a good choice for Atlas-based volumetric analysis of basal ganglia.

A Multi-Modal and Multi-Atlas Integrated Framework for Identification of Mild Cognitive Impairment

BACKGROUND

Multi-modal neuroimaging with appropriate atlas is vital for effectively differentiating mild cognitive impairment (MCI) from healthy controls (HC).

METHODS

The resting-state functional magnetic resonance imaging (rs-fMRI) and structural MRI (sMRI) of 69 MCI patients and 61 HC subjects were collected. Then, the gray matter volumes obtained from the sMRI and Hurst exponent (HE) values calculated from rs-fMRI data in the Automated Anatomical Labeling (AAL-90), Brainnetome (BN-246), Harvard-Oxford (HOA-112) and AAL3-170 atlases were extracted, respectively. Next, these characteristics were selected with a minimal redundancy maximal relevance algorithm and a sequential feature collection method in single or multi-modalities, and only the optimal features were retained after this procedure. Lastly, the retained characteristics were served as the input features for the support vector machine (SVM)-based method to classify MCI patients, and the performance was estimated with a leave-one-out cross-validation (LOOCV).

RESULTS

Our proposed method obtained the best 92.00% accuracy, 94.92% specificity and 89.39% sensitivity with the sMRI in AAL-90 and the fMRI in HOA-112 atlas, which was much better than using the single-modal or single-atlas features.

CONCLUSION

The results demonstrated that the multi-modal and multi-atlas integrated method could effectively recognize MCI patients, which could be extended into various neurological and neuropsychiatric diseases.

Machine Learning and rs-fMRI to Identify Potential Brain Regions Associated with Autism Severity

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized primarily by social impairments that manifest in different severity levels. In recent years, many studies have explored the use of machine learning (ML) and resting-state functional magnetic resonance images (rs-fMRI) to investigate the disorder. These approaches evaluate brain oxygen levels to indirectly measure brain activity and compare typical developmental subjects with ASD ones. However, none of these works have tried to classify the subjects into severity groups using ML exclusively applied to rs-fMRI data. Information on ASD severity is frequently available since some tools used to support ASD diagnosis also include a severity measurement as their outcomes. The aforesaid is the case of the Autism Diagnostic Observation Schedule (ADOS), which splits the diagnosis into three groups: ‘autism’, ‘autism spectrum’, and ‘non-ASD’. Therefore, this paper aims to use ML and fMRI to identify potential brain regions as biomarkers of ASD severity. We used the ADOS score as a severity measurement standard. The experiment used fMRI data of 202 subjects with an ASD diagnosis and their ADOS scores available at the ABIDE I consortium to determine the correct ASD sub-class for each one. Our results suggest a functional difference between the ASD sub-classes by reaching 73.8% accuracy on cingulum regions. The aforementioned shows the feasibility of classifying and characterizing ASD using rs-fMRI data, indicating potential areas that could lead to severity biomarkers in further research. However, we highlight the need for more studies to confirm our findings.

A Reparametrized CNN Model to Distinguish Alzheimer's Disease Applying Multiple Morphological Metrics and Deep Semantic Features From Structural MRI

It is of potential clinical value to improve the accuracy of Alzheimer's disease (AD) recognition using structural MRI. We proposed a reparametrized convolutional neural network (Re-CNN) to discriminate AD from NC by applying morphological metrics and deep semantic features. The deep semantic features were extracted through Re-CNN on structural MRI. Considering the high redundancy in deep semantic features, we constrained the similarity of the features and retained the most distinguishing features utilizing the reparametrized module. The Re-CNN model was trained in an end-to-end manner on structural MRI from the ADNI dataset and tested on structural MRI from the AIBL dataset. Our proposed model achieves better performance over some existing structural MRI-based AD recognition models. The experimental results show that morphological metrics along with the constrained deep semantic features can relatively improve AD recognition performance. Our code is available at: https://github.com/czp19940707/Re-CNN.

Unlocking the Memory Component of Alzheimer’s Disease:Biological Processes and Pathways across Brain Regions

Alzheimer’s Disease (AD) is a neurodegenerative disorder characterized by a progressive loss of memory and a general cognitive decline leading to dementia. AD is characterized by changes in the behavior of the genome and can be traced across multiple brain regions and cell types. It is mainly associated with β-amyloid deposits and tau protein misfolding, leading to neurofibrillary tangles. In recent years, however, research has shown that there is a high complexity of mechanisms involved in AD neurophysiology and functional decline enabling its diverse presentation and allowing more questions to arise. In this study, we present a computational approach to facilitate brain region-specific analysis of genes and biological processes involved in the memory process in AD. Utilizing current genetic knowledge we provide a gene set of 265 memory-associated genes in AD, combinations of which can be found co-expressed in 11 different brain regions along with their functional role. The identified genes participate in a spectrum of biological processes ranging from structural and neuronal communication to epigenetic alterations and immune system responses. These findings provide new insights into the molecular background of AD and can be used to bridge the genotype–phenotype gap and allow for new therapeutic hypotheses.

Characterizing Global and Regional Brain Structures in Amnestic Mild Cognitive Impairment Among Rural Residents: A Population-Based Study

Background: Structural brain magnetic resonance imaging (MRI) scans may provide reliable neuroimaging markers for defining amnestic mild cognitive impairment (aMCI). Objective: We sought to characterize global and regional brain structures of aMCI among rural-dwelling older adults with limited education in China. Methods: This population-based study included 180 participants (aged≥65 years, 42 with aMCI and 138 normal controls) in the Shandong Yanggu Study of Aging and Dementia during 2014–2016. We defined aMCI following the Petersen’s criteria. Global and regional brain volumes were automatically segmented on MRI scans and compared using a region-of-interest approach. Data were analyzed using general linear regression models. Results: Multi-adjusted β-coefficient (95% confidence interval) of brain volumes (cm3) associated with aMCI was –12.07 (–21.49, –2.64) for global grey matter (GM), –18.31 (–28.45, –8.17) for global white matter (WM), 28.17 (12.83, 44.07) for cerebrospinal fluid (CSF), and 2.20 (0.24, 4.16) for white matter hyperintensities (WMH). Furthermore, aMCI was significantly associated with lower GM volumes in bilateral superior temporal gyri, thalamus and right cuneus, and lower WM volumes in lateral areas extending from the frontal to the parietal, temporal, and occipital lobes, as well as right hippocampus (p < 0.05). Conclusion: Brain structure of older adults with aMCI is characterized by reduced global GM and WM volumes, enlarged CSF volume, increased WMH burden, reduced GM volumes in bilateral superior temporal gyri, thalamus, and right cuneus, and widespread reductions of lateral WM volumes.

Brain functional network integrity sustains cognitive function despite atrophy in presymptomatic genetic frontotemporal dementia

INTRODUCTION

The presymptomatic phase of neurodegenerative disease can last many years, with sustained cognitive function despite progressive atrophy. We investigate this phenomenon in familial frontotemporal dementia (FTD).

METHODS

We studied 121 presymptomatic FTD mutation carriers and 134 family members without mutations, using multivariate data-driven approach to link cognitive performance with both structural and functional magnetic resonance imaging. Atrophy and brain network connectivity were compared between groups, in relation to the time from expected symptom onset.

RESULTS

There were group differences in brain structure and function, in the absence of differences in cognitive performance. Specifically, we identified behaviorally relevant structural and functional network differences. Structure-function relationships were similar in both groups, but coupling between functional connectivity and cognition was stronger for carriers than for non-carriers, and increased with proximity to the expected onset of disease.

DISCUSSION

Our findings suggest that the maintenance of functional network connectivity enables carriers to maintain cognitive performance.

Atypical Resting-State Functional Connectivity Dynamics Correlate With Early Cognitive Dysfunction in HIV Infection

Purpose: Previous studies have shown that HIV affects striato-cortical regions, leading to persisting cognitive impairment in 30-70% of the infected individuals despite combination antiretroviral therapy. This study aimed to investigate brain functional dynamics whose deficits might link to early cognitive decline or immunologic deterioration. Methods: We applied sliding windows and K-means clustering to fMRI data (HIV patients with asymptomatic neurocognitive impairment and controls) to construct dynamic resting-state functional connectivity (RSFC) maps and identify states of their reoccurrences. The average and variability of dynamic RSFC, and the dwelling time and state transitioning of each state were evaluated. Results: HIV patients demonstrated greater variability in RSFC between the left pallidum and regions of right pre-central and post-central gyri, and between the right supramarginal gyrus and regions of the right putamen and left pallidum. Greater variability was also found in the frontal RSFC of pars orbitalis of the left inferior frontal gyrus and right superior frontal gyrus (medial). While deficits in learning and memory recall of HIV patients related to greater striato-sensorimotor variability, deficits in attention and working memory were associated with greater frontal variability. Greater striato-parietal variability presented a strong link with immunologic function (CD4+/CD8+ ratio). Furthermore, HIV-infected patients exhibited longer time and reduced transitioning in states typified by weaker connectivity in specific networks. CD4+T-cell counts of the HIV-patients were related to reduced state transitioning. Conclusion: Our findings suggest that HIV alters brain functional connectivity dynamics, which may underlie early cognitive impairment. These findings provide novel insights into our understanding of HIV pathology, complementing the existing knowledge.

Machine Learning Detects Pattern of Differences in Functional Magnetic Resonance Imaging (fMRI) Data between Chronic Fatigue Syndrome (CFS) and Gulf War Illness (GWI)

BACKGROUND

Gulf War Illness (GWI) and Chronic Fatigue Syndrome (CFS) are two debilitating disorders that share similar symptoms of chronic pain, fatigue, and exertional exhaustion after exercise. Many physicians continue to believe that both are psychosomatic disorders and to date no underlying etiology has been discovered. As such, uncovering objective biomarkers is important to lend credibility to criteria for diagnosis and to help differentiate the two disorders.

METHODS

We assessed cognitive differences in 80 subjects with GWI and 38 with CFS by comparing corresponding fMRI scans during 2-back working memory tasks before and after exercise to model brain activation during normal activity and after exertional exhaustion, respectively. Voxels were grouped by the count of total activity into the Automated Anatomical Labeling (AAL) atlas and used in an "ensemble" series of machine learning algorithms to assess if a multi-regional pattern of differences in the fMRI scans could be detected.

RESULTS

A K-Nearest Neighbor (70%/81%), Linear Support Vector Machine (SVM) (70%/77%), Decision Tree (82%/82%), Random Forest (77%/78%), AdaBoost (69%/81%), Naïve Bayes (74%/78%), Quadratic Discriminant Analysis (QDA) (73%/75%), Logistic Regression model (82%/82%), and Neural Net (76%/77%) were able to differentiate CFS from GWI before and after exercise with an average of 75% accuracy in predictions across all models before exercise and 79% after exercise. An iterative feature selection and removal process based on Recursive Feature Elimination (RFE) and Random Forest importance selected 30 regions before exercise and 33 regions after exercise that differentiated CFS from GWI across all models, and produced the ultimate best accuracies of 82% before exercise and 82% after exercise by Logistic Regression or Decision Tree by a single model, and 100% before and after exercise when selected by any six or more models. Differential activation on both days included the right anterior insula, left putamen, and bilateral orbital frontal, ventrolateral prefrontal cortex, superior, inferior, and precuneus (medial) parietal, and lateral temporal regions. Day 2 had the cerebellum, left supplementary motor area and bilateral pre- and post-central gyri. Changes between days included the right Rolandic operculum switching to the left on Day 2, and the bilateral midcingulum switching to the left anterior cingulum.

CONCLUSION

We concluded that CFS and GWI are significantly differentiable using a pattern of fMRI activity based on an ensemble machine learning model.

Multi-modal latent space inducing ensemble SVM classifier for early dementia diagnosis with neuroimaging data

Fusing multi-modality data is crucial for accurate identification of brain disorder as different modalities can provide complementary perspectives of complex neurodegenerative disease. However, there are at least four common issues associated with the existing fusion methods. First, many existing fusion methods simply concatenate features from each modality without considering the correlations among different modalities. Second, most existing methods often make prediction based on a single classifier, which might not be able to address the heterogeneity of the Alzheimer's disease (AD) progression. Third, many existing methods often employ feature selection (or reduction) and classifier training in two independent steps, without considering the fact that the two pipelined steps are highly related to each other. Forth, there are missing neuroimaging data for some of the participants (e.g., missing PET data), due to the participants' "no-show" or dropout. In this paper, to address the above issues, we propose an early AD diagnosis framework via novel multi-modality latent space inducing ensemble SVM classifier. Specifically, we first project the neuroimaging data from different modalities into a latent space, and then map the learned latent representations into the label space to learn multiple diversified classifiers. Finally, we obtain the more reliable classification results by using an ensemble strategy. More importantly, we present a Complete Multi-modality Latent Space (CMLS) learning model for complete multi-modality data and also an Incomplete Multi-modality Latent Space (IMLS) learning model for incomplete multi-modality data. Extensive experiments using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset have demonstrated that our proposed models outperform other state-of-the-art methods.

Cerebrospinal fluid T1 value phantom reproduction at scan room temperature

The T1 value of pure water, which is often used as a phantom to simulate cerebrospinal fluid, is significantly different from that of in-vivo cerebrospinal fluid. The purpose of this study was to develop a phantom with a T1 value equivalent to that of in-vivo cerebrospinal fluid under examination room temperature (23°C-25°C). In this study, 1.5 and 3.0 T magnetic resonance imaging scanners were used. We examined the signal intensity change in relation to pure water temperature, the T1 values of acetone-diluted solutions (0-100 v/v%, in 10 steps), and the correlation coefficients obtained from volunteers and the prepared phantoms. The T1 value was close to the value reported in the literature for cerebrospinal fluid when the acetone-diluted solution was 70 v/v% or higher at scan room temperature. The value at that time was 3532.81-4704.57 ms at 1.5 T and it ranged from 4052.41 to 5701.61 ms at 3.0 T. The highest correlation with the values obtained from the volunteers was r = 0.993 with pure acetone at 1.5 T and r = 0.991 with acetone 90 v/v% at 3.0 T. The relative error of the best phantom-volunteer match was 32.61 (%) ± 6.71 at 1.5 T and 46.67 (%) ± 4.31 at 3.0 T. The T1 value measured by the null point method did not detect a significant difference between in vivo CSF and acetone 100 v/v% at 1.5 T and acetone 90 v/v% at 3.0 T. The T1 value of cerebrospinal fluid in the living body at scan room temperature was reproduced with acetone. The optimum concentration of acetone for cerebrospinal-fluid reproduction was pure acetone at 1.5 T and 90 v/v% at 3.0 T.