Cytotoxic lesion of corpus callosum after COVID-19 vaccination: case report

PURPOSE

Cytotoxic lesions of corpus callosum (CLOCCs) are associated with many disease entities. Serious neurological complications after coronavirus disease 2019 (COVID-19) vaccination are rare.

CASE REPORT

A 20-year-old man presented with severe headache for 2 days. He had received the first dose of ChAdOx1nCoV-19 COVID-19 vaccine 5 days ago. Persistent dull headache occurred on the third day after vaccination and intensified gradually to awaken him from sleep at night. No neck stiffness was observed. Brain magnetic resonance angiography (MRA) 9 days after vaccination revealed an oval-shaped diffusion-weighted restriction lesion at the splenium of corpus callosum with a mildly high signal intensity on T2-weighted images (T2WI) and low signal intensity on apparent diffusion coefficient (ADC) imaging but without enhancement after contrast injection. A COVID-19 polymerase chain reaction test was negative. A blood test revealed slight leukocytopenia, thrombocytopenia, and hyponatremia. Further autoimmune and hematological studies were normal. A cerebrospinal fluid study revealed normal intracranial pressure. The patient's headache improved gradually. Follow-up brain MRA 5 weeks after vaccination revealed complete disappearance of the diffusion-weighted restriction lesion of the splenium.

CONCLUSION

CLOCCs are rare transient adverse effect of COVID-19 vaccination possibly related to a cytokine storm. The splenic lesion might disappear spontaneously with a good prognosis.

B 1 + inhomogeneity mitigation for diffusion weighted MRI at 7T using TR-FOCI pulses

PURPOSE

The purpose of this study is to improve the image quality of diffusion-weighted images obtained with a single RF transmit channel 7 T MRI setup using time-resampled frequency-offset corrected inversion (TR-FOCI) pulses to refocus the spins in a twice-refocused spin-echo readout scheme.

METHODS

We replaced the conventional Shinnar-Le Roux-pulses in the twice refocused diffusion sequence with TR-FOCI pulses. The slice profiles were evaluated in simulation and experimentally in phantoms. The image quality was evaluated in vivo comparing the Shinnar-Le Roux and TR-FOCI implementation using a b value of 0 and of 1000 s/mm2.

RESULTS

The b0 and diffusion-weighted images acquired using the modified sequence improved the image quality across the whole brain. A region of interest-based analysis showed an SNR increase of 113% and 66% for the nondiffusion-weighted (b0) and the diffusion-weighted (b = 1000 s/mm2) images in the temporal lobes, respectively. Investigation of all slices showed that the adiabatic pulses mitigatedB 1 + $$ {B}_1^{+} $$ inhomogeneity globally using a conventional single-channel transmission setup.

CONCLUSION

The TR-FOCI pulse can be used in a twice-refocused spin-echo diffusion pulse sequence to mitigate the impact ofB 1 + $$ {B}_1^{+} $$ inhomogeneity on the signal intensity across the brain at 7 T. However, further work is needed to address SAR limitations.

Diffusion tensor imaging within the healthy cervical spinal cord: Within- participants reliability and measurement error

BACKGROUND

Diffusion tensor imaging (DTI) is a promising technique for the visualization of the cervical spinal cord (CSC) in vivo. It provides information about the tissue structure of axonal white matter, and it is thought to be more sensitive than other MR imaging techniques for the evaluation of damage to tracts in the spinal cord.

AIM

The purpose of this study was to determine the within-participants reliability and error magnitude of measurements of DTI metrics in healthy human CSC.

METHODS

A total of twenty healthy controls (10 male, mean age: 33.9 ± 3.5 years, 10 females, mean age: 47.5 ± 14.4 years), with no family history of any neurological disorders or a contraindication to MRI scanning were recruited over a period of two months. Each participant was scanned twice with an MRI 3 T scanner using standard DTI sequences. Spinal Cord Toolbox (SCT) software was used for image post-processing. Data were first corrected for motion artefact, then segmented, registered to a template, and then the DTI metrics were computed. The within-participants coefficients of variation (CV%), the single and average within-participants intraclass correlation coefficients (ICC) and Bland-Altman plots for WM, VC, DC and LC fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were determined for the cervical spinal cord (between the 2nd and 5th cervical vertebrae).

RESULTS

DTI metrics showed poor to excellent within-participants reliability for both single and average ICC and moderate to high reproducibility for CV%, all variation dependent on the location of the ROI. The BA plots showed good within-participants agreement between the scan-rescan values.

CONCLUSION

Results from this reliability study demonstrate that clinical trials using the DTI technique are feasible and that DTI, in particular regions of the cord is suitable for use for the monitoring of degenerative WM changes.

Diffusion-derived vessel density (DDVD) computed from a simple diffusion MRI protocol as a biomarker of placental blood circulation in patients with placenta accreta spectrum disorders: A proof-of-concept study

OBJECTIVES

Increasing trend of PAS (placenta accreta spectrum disorders) incidence is a major health concern as PAS is associated with high maternal morbidity and mortality during cesarean section. Prenatal identification of PAS is crucial for delivery planning and patients management. This study aims to explore whether diffusion-derived vessel density (DDVD) computed from a simple diffusion MRI protocol differs in PAS from normal placenta.

METHODS

We enrolled 86 patients with PAS disorders and 40 pregnant women without PAS disorders. Each patient underwent intravoxel incoherent motion (IVIM) MRI sequence with 11 b-values. Placenta diffusion-derived vessel density (DDVD-b0b50) was the signal difference between b = 0 and b = 50 s/mm2 images. DDVD(b0b50) A/N was calculated as [accreta lesion DDVD(b0b50)]/ [normal placenta DDVD(b0b50)]. The correlation between DDVD and gestational age was explored using Spearman rank correlation. Differences of DDVD(b0b50) A/N in patients with normal placentas and with PAS, and in patients with different subtypes of PAS were explored.

RESULTS

DDVD was negatively correlated with gestational age (p = 0.023, r = -0.359) in patients with normal placentas. DDVD(b0b50) A/N was significantly higher in patients with PAS (median:1.16, mean: 1.261) than normal placenta (median:1.02, mean: 1.032, p < 0.001) and especially higher in patients with placenta increta (median:1.14, mean: 1.278) and percreta (median: 1.20, mean: 1.396, p < 0.001).

CONCLUSION

As a higher DDVD indicates higher physiological volume of micro-vessels in PAS, this study suggests DDVD can be a potential biomarker to evaluate the placenta perfusion.

Multi-shell diffusion MRI of the fornix as a biomarker for cognition in Alzheimer's disease

BACKGROUND AND PURPOSE

A substantial fraction of those who had Alzheimer's Disease (AD) pathology on autopsy did not have dementia in life. While biomarkers for AD pathology are well-developed, biomarkers specific to cognitive domains affected by early AD are lagging. Diffusion MRI (dMRI) of the fornix is a candidate biomarker for early AD-related cognitive changes but is susceptible to bias due to partial volume averaging (PVA) with cerebrospinal fluid. The purpose of this work is to leverage multi-shell dMRI to correct for PVA and to evaluate PVA-corrected dMRI measures in fornix as a biomarker for cognition in AD.

METHODS

Thirty-three participants in the Cleveland Alzheimer's Disease Research Center (CADRC) (19 with normal cognition (NC), 10 with mild cognitive impairment (MCI), 4 with dementia due to AD) were enrolled in this study. Multi-shell dMRI was acquired, and voxelwise fits were performed with two models: 1) diffusion tensor imaging (DTI) that was corrected for PVA and 2) neurite orientation dispersion and density imaging (NODDI). Values of tissue integrity in fornix were correlated with neuropsychological scores taken from the Uniform Data Set (UDS), including the UDS Global Composite 5 score (UDSGC5).

RESULTS

Statistically significant correlations were found between the UDSGC5 and PVA-corrected measure of mean diffusivity (MDc, r = -0.35, p < 0.05) from DTI and the intracelluar volume fraction (ficvf, r = 0.37, p < 0.04) from NODDI. A sensitivity analysis showed that the relationship to MDc was driven by episodic memory, which is often affected early in AD, and language.

CONCLUSION

This cross-sectional study suggests that multi-shell dMRI of the fornix that has been corrected for PVA is a potential biomarker for early cognitive domain changes in AD. A longitudinal study will be necessary to determine if the imaging measure can predict cognitive decline.

Associations of depression and perceived physical fatigability with white matter integrity in older adults

BACKGROUNDS

Fatigability is prevalent in older adults. However, it is often associated with depressed mood. We aim to investigate these two psychobehavioral constructs by examining their underpinning of white matter structures in the brain and their associations with different medical conditions.

METHODS

Twenty-seven older adults with late-life depression (LLD) and 34 cognitively normal controls (CN) underwent multi-shell diffusion MRI. Fatigability was measured with the Pittsburgh Fatigability Scale. We examined white matter integrity by measuring the quantitative anisotropy (QA), a fiber tracking parameter with better accuracy than the traditional imaging technique.

RESULTS

We found those with LLD had lower QA in the 2nd branch of the left superior longitudinal fasciculus (SLF-II), and those with more physical fatigability had lower QA in more widespread brain regions. In tracts associated with more physical fatigability, the lower QA in left acoustic radiation and left superior thalamic radiation correlated with higher blood glucose (r = - 0.46 and - 0.49). In tracts associated with depression, lower QA in left SLF-II correlated with higher bilirubin level (r = - 0.58).

DISCUSSION

Depression and fatigability were associated with various white matter integrity changes, which correlated with biochemistry biomarkers all related to inflammation.

White matter structural changes before and after microvascular decompression for hemifacial spasm

Hemifacial spasm (HFS) is a syndrome characterized by involuntary contractions of the facial muscles innervated by the ipsilateral facial nerve. Currently, microvascular decompression (MVD) is an effective treatment for HFS. Diffusion weighted imaging (DWI) is a non-invasive advanced magnetic resonance technique that allows us to reconstruct white matter (WM) virtually based on water diffusion direction. This enables us to model the human brain as a complex network using graph theory. In our study, we recruited 32 patients with HFS and 32 healthy controls to analyze and compare the topological organization of whole-brain white matter networks between the groups. We also explored the potential relationships between altered topological properties and clinical outcomes. Compared to the HC group, the white matter network was disrupted in both preoperative and postoperative groups of HFS patients, mainly located in the somatomotor network, limbic network, and default network (All P < 0.05, FDR corrected). There was no significant difference between the preoperative and postoperative groups (P > 0.05, FDR corrected). There was a correlation between the altered topological properties and clinical outcomes in the postoperative group of patients (All P < 0.05, FDR corrected). Our findings indicate that in HFS, the white matter structural network was disrupted before and after MVD, and that these alterations in the postoperative group were correlated with the clinical outcomes. White matter alteration here described may subserve as potential biomarkers for HFS and may help us identify patients with HFS who can benefit from MVD and thus can help us make a proper surgical patient selection.

Altered DTI scalars in the hippocampus are associated with morphological and structural changes after traumatic brain injury

Blunt and diffuse injury is a highly prevalent form of traumatic brain injury (TBI) which can result in microstructural alterations in the brain. The blunt impact on the brain can affect the immediate contact region but can also affect the vulnerable regions like hippocampus, leading to functional impairment and long-lasting cognitive deficits. The hippocampus of the moderate weight drop injured male rats was longitudinally assessed for microstructural changes using in vivo MR imaging from 4 h to Day 30 post-injury (PI). The DTI analysis found a prominent decline in the apparent diffusion coefficient (ADC), radial diffusivity (RD), and axial diffusivity (AD) values after injury. The perturbed DTI scalars accompanied histological changes in the hippocampus, wherein both the microglia and astrocytes showed changes in the morphometric parameters at all timepoints. Along with this, the hippocampus showed presence of Aβ positive fibrils and neurite plaques after injury. Therefore, this study concludes that TBI can lead to a complex morphological, cellular, and structural alteration in the hippocampus which can be diagnosed using in vivo MR imaging techniques to prevent long-term functional deficits.

TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance

We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise tissue microstructure measurements. By employing a point cloud representation, TractGeoNet can directly utilize tissue microstructure and positional information from all points within a fiber tract without the need to average or bin data along the streamline as traditionally required by dMRI tractometry methods. To improve regression performance, we propose a novel loss function, the Paired-Siamese Regression loss, which encourages the model to focus on accurately predicting the relative differences between regression label scores rather than just their absolute values. In addition, to gain insight into the brain regions that contribute most strongly to the prediction results, we propose a Critical Region Localization algorithm. This algorithm identifies highly predictive anatomical regions within the white matter fiber tracts for the regression task. We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language using a dataset of 20 association white matter fiber tracts from 806 subjects from the Human Connectome Project Young Adult dataset. The results demonstrate superior prediction performance of TractGeoNet compared to several popular regression models that have been applied to predict individual cognitive performance based on neuroimaging features. Of the twenty tracts studied, we find that the left arcuate fasciculus tract is the most highly predictive of the two studied language performance assessments. Within each tract, we localize critical regions whose microstructure and point information are highly and consistently predictive of language performance across different subjects and across multiple independently trained models. These critical regions are widespread and distributed across both hemispheres and all cerebral lobes, including areas of the brain considered important for language function such as superior and anterior temporal regions, pars opercularis, and precentral gyrus. Overall, TractGeoNet demonstrates the potential of geometric deep learning to enhance the study of the brain's white matter fiber tracts and to relate their structure to human traits such as language performance.

A comparative study of diffusion kurtosis imaging and diffusion tensor imaging in detecting corticospinal tract impairment in diffuse glioma patients

PURPOSE

This study aimed to investigate the diagnostic performance of diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in identifying aberrations in the corticospinal tract (CST), whilst elucidating the relationship between abnormalities of CST and patients' motor function.

METHODS

Altogether 21 patients with WHO grade II or grade IV glioma were enrolled and divided into Group 1 and Group 2, according to the presence or absence of preoperative paralysis. DKI and DTI metrics were generated and projected onto the CST. Histograms of the CST along x, y, and z axes were developed based on DKI and DTI metrics, and compared subsequently to determine regions of aberrations on the fibers. The receiver operating characteristic curve was performed to investigate the diagnostic efficacy of DKI and DTI metrics.

RESULTS

In Group 1, a significantly lower fractional anisotropy, radial kurtosis and mean kurtosis, and a higher mean diffusivity were found in the ipsilateral CST as compared to the contralateral CST. Significantly higher relative axial diffusivity, relative radial diffusivity, and relative mean diffusivity (rMD) were found in Group 1, as compared to Group 2. The relative volume of ipsilateral CST abnormalities higher than the maximum value of mean kurtosis combined with rMD exhibited the best diagnostic performance in distinguishing dysfunction of CST with an AUC of 0.93.

CONCLUSION

DKI is sensitive in detecting subtle changes of CST distal from the tumor. The combination of DKI and DTI is feasible for evaluating the impairment of the CST.

The Transatlantic Recommendations for Prostate Gland Evaluation with Magnetic Resonance Imaging After Focal Therapy (TARGET): A Systematic Review and International Consensus Recommendations

BACKGROUND AND OBJECTIVE

Magnetic resonance imaging (MRI) can detect recurrences after focal therapy for prostate cancer but there is no robust guidance regarding its use. Our objective was to produce consensus recommendations on MRI acquisition, interpretation, and reporting after focal therapy.

METHODS

A systematic review was performed in July 2022 to develop consensus statements. A two-round consensus exercise was then performed, with a consensus meeting in January 2023, during which 329 statements were scored by 23 panellists from Europe and North America spanning urology, radiology, and pathology with experience across eight focal therapy modalities. Using RAND Corporation/University of California-Los Angeles methodology, the Transatlantic Recommendations for Prostate Gland Evaluation with MRI after Focal Therapy (TARGET) were based on consensus for statements scored with agreement or disagreement.

KEY FINDINGS AND LIMITATIONS

In total, 73 studies were included in the review. All 20 studies (100%) reporting suspicious imaging features cited focal contrast enhancement as suspicious for cancer recurrence. Of 31 studies reporting MRI assessment criteria, the Prostate Imaging-Reporting and Data System (PI-RADS) score was the scheme used most often (20 studies; 65%), followed by a 5-point Likert score (six studies; 19%). For the consensus exercise, consensus for statements scored with agreement or disagreement increased from 227 of 295 statements (76.9%) in round one to 270 of 329 statements (82.1%) in round two. Key recommendations include performing routine MRI at 12 mo using a multiparametric protocol compliant with PI-RADS version 2.1 standards. PI-RADS category scores for assessing recurrence within the ablation zone should be avoided. An alternative 5-point scoring system is presented that includes a major dynamic contrast enhancement (DCE) sequence and joint minor diffusion-weighted imaging and T2-weighted sequences. For the DCE sequence, focal nodular strong early enhancement was the most suspicious imaging finding. A structured minimum reporting data set and minimum reporting standards for studies detailing MRI data after focal therapy are presented.

CONCLUSIONS AND CLINICAL IMPLICATIONS

The TARGET consensus recommendations may improve MRI acquisition, interpretation, and reporting after focal therapy for prostate cancer and provide minimum standards for study reporting.

PATIENT SUMMARY

Magnetic resonance imaging (MRI) scans can detect recurrent of prostate cancer after focal treatments, but there is a lack of guidance on MRI use for this purpose. We report new expert recommendations that may improve practice.

Multispecies comparative prostate anatomy by imaging: Implications for experimental models of prostatic disease

BACKGROUND

There is an increasing interest in using preclinical models for development and assessment of medical devices and imaging techniques for prostatic disease care. Still, a comprehensive assessment of the prostate's radiological anatomy in primary preclinical models such as dogs, rabbits, and mice utilizing human anatomy as a reference point remains necessary with no optimal model for each purpose being clearly defined in the literature. Therefore, this study compares the anatomical characteristics of different animal models to the human prostatic gland from the imaging perspective.

METHODS

We imaged five Beagle laboratory dogs, five New Zealand White rabbits, and five mice, all sexually mature males, under Institutional Animal Care and Use Committee (IACUC) approval. Ultrasonography (US) was performed using the Vevo® F2 for mice (57 MHz probe). Rabbits and dogs were imaged using the Siemens® Acuson S3000 (17 MHz probe) and endocavitary (8 MHz) probes, respectively. Magnetic resonance imaging (MRI) was also conducted with a 7T scanner in mice and 3T scanner in rabbits and dogs.

RESULTS

Canine transrectal US emerged as the optimal method for US imaging, depicting a morphologically similar gland to humans but lacking echoic zonal differentiation. MRI findings in canines indicated a homogeneously structured gland similar to the human peripheral zone on T2-weighted images (T2W) and apparent diffusion coefficient (ADC). In rabbits, US imaging faced challenges due to the pubic symphysis, whereas MRI effectively visualized all structures with the prostate presenting a similar aspect to the human peripheral gland on T2W and ADC maps. Murine prostate assessment revealed poor visualization of the prostate glands in ultrasound due to its small size, while 7T MRI delineated the distinct prostates and its lobes, with the lateral and dorsal prostate resembling the peripheral zone and the anterior prostate the central zone of the human gland.

CONCLUSION

Dogs stand out as superior models for advanced preclinical studies in prostatic disease research. However, mice present as a good model for early stage studies and rabbits are a cost-effective alternative and serve as valuable tools in specific research domains when canine research is not feasible.

Thalamic segmentation based on diffusion tensor imaging in patients with trigeminal neuralgia

Classical trigeminal neuralgia (CTN) refers to episodic pain that is strictly confined to the trigeminal distribution area, and the thalamus is an important component of the trigeminal sensory pathway. Probabilistic tracking imaging algorithm was used to identify specific connections between the thalamus and the cortex, in order to identify structural changes in the thalamus of patients with CTN and perform thalamic segmentation. A total of 32 patients with CTN and 32 healthy controls underwent DTI-MRI scanning (3.0 T). Differences in fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) between the groups were studied. Correlation analysis was performed with clinical course and pain level. Compared to the healthy controls, patients in the CTN group had significantly reduced FA, increased AD, RD and MD in somatosensory subregion of the bilateral thalamus, increased RD in frontal subregion, increased RD and MD in motor subregion. Correlation analysis showed that patient history was positively correlated with pain grading, and that medical history was positively correlated with significantly reduced FA in somatosensory subregion, negatively correlated with increased RD and MD in motor subregion. We used DTI-based probabilistic fiber tracking to discover altered structural connectivity between the thalamus and cerebral cortex in patients with CTN and to obtain a thalamic segmentation atlas, which will help to further understand the pathophysiology of CTN and serve as a future reference for thalamic deep brain stimulation electrode implantation for the treatment of intractable pain.

Transformer based multi-modal MRI fusion for prediction of post-menstrual age and neonatal brain development analysis

The brain development during the perinatal period is characterized by rapid changes in both structure and function, which have significant impact on the cognitive and behavioral abilities later in life. Accurate assessment of brain age is a crucial indicator for brain development maturity and can help predict the risk of neonatal pathology. However, evaluating neonatal brains using magnetic resonance imaging (MRI) is challenging due to its complexity, multi-dimension, and noise with subtle alterations. In this paper, we propose a multi-modal deep learning framework based on transformers for precise post-menstrual age (PMA) estimation and brain development analysis using T2-weighted structural MRI (T2-sMRI) and diffusion MRI (dMRI) data. First, we build a two-stream dense network to learn modality-specific features from T2-sMRI and dMRI of brain individually. Then, a transformer module based on self-attention mechanism integrates these features for PMA prediction and preterm/term classification. Finally, saliency maps on brain templates are used to enhance the interpretability of results. Our method is evaluated on the multi-modal MRI dataset of the developing Human Connectome Project (dHCP), which contains 592 neonates, including 478 term-born and 114 preterm-born subjects. The results demonstrate that our method achieves a 0.5-week mean absolute error (MAE) in PMA estimation for term-born subjects. Notably, preterm-born subjects exhibit delayed brain development, worsening with increasing prematurity. Our method also achieves 95% accuracy in classification of term-born and preterm-born subjects, revealing significant group differences.

Surgical evidence-based comparison of [68Ga]Ga-FAPI-04 PET and MRI-DWI for assisting debulking surgery in ovarian cancer patients

PURPOSE

Imaging assessment of abdominopelvic tumor burden is crucial for debulking surgery decision in ovarian cancer patients. This study aims to compare the efficiency of [68Ga]Ga-FAPI-04 FAPI PET and MRI-DWI in the preoperative evaluation and its potential impact to debulking surgery decision.

METHODS

Thirty-six patients with suspected/confirmed ovarian cancer were enrolled and underwent integrated [68Ga]Ga-FAPI-04 PET/MRI. Nineteen patients (15 stage III-IV and 4 I-II stage) who underwent debulking surgery were involved in the diagnostic efficiency analysis. The images of [68Ga]Ga-FAPI-04 PET and MRI-DWI were visually analyzed respectively. Immunohistochemistry on FAP was performed in metastatic lesions to investigate the radiological missing of [68Ga]Ga-FAPI-04 PET as well as its different performance in primary debulking surgery (PDS) and interval debulking surgery (IDS) patients. Potential imaging impact on management was also studied in 35 confirmed ovarian cancer patients.

RESULTS

[68Ga]Ga-FAPI-04 PET displayed higher sensitivity (76.8% vs.59.9%), higher accuracy (84.9% vs. 80.7%), and lower missing rate (23.2% vs. 40.1%) than MRI-DWI in detecting abdominopelvic metastasis. The diagnostic superiority of [68Ga]Ga-FAPI-04 PET is more obvious in PDS patients but diminished in IDS patients. [68Ga]Ga-FAPI-04 PET outperformed MRI-DWI in 70.8% abdominopelvic regions (17/24), which contained seven key regions that impact the resectability and surgical complexity. MRI-DWI hold advantage in the peritoneal surface of the bladder and the central tendon of the diaphragm. Of the contradictory judgments between the two modalities (14.9%), [68Ga]Ga-FAPI-04 PET correctly identified more lesions, particularly in PDS patients (73.8%). In addition, FAP expression was independent of lesion size and decreased in IDS patients. [68Ga]Ga-FAPI-04 PET changed 42% of surgical planning that was previously based on MRI-DWI.

CONCLUSION

[68Ga]Ga-FAPI-04 PET is more efficient in assisting debulking surgery in ovarian cancer patients than MRI-DWI. Integrated [68Ga]Ga-FAPI-04 PET/MR imaging is a potential method for planning debulking surgery in ovarian cancer patients.

Understanding Wellbeing Profiles According to White Matter Structural Connectivity Sub-types in Early Adolescents: The First Hundred Brains Cohort from the Longitudinal Adolescent Brain Study

Wellbeing is protective against the emergence of psychopathology. Neurobiological markers associated with mental wellbeing during adolescence are important to understand. Limited research has examined neural networks (white matter tracts) and mental wellbeing in early adolescence specifically. A cross-sectional diffusion tensor imaging analysis approach was conducted, from the Longitudinal Adolescent Brain study, First Hundred Brains cohort (N = 99; 46.5% female; Mage = 13.01, SD = 0.55). Participants completed self-report measures including wellbeing, quality-of-life, and psychological distress. Potential neurobiological profiles using fractional anisotropy, axial, and radial diffusivity were determined via a whole brain voxel-wise approach, and hierarchical cluster analysis of fractional anisotropy values, obtained from 21 major white matter tracts. Three cluster groups with significantly different neurobiological profiles were distinguished. No significant differences were found between the three cluster groups and measures of wellbeing, but two left lateralized significant associations between white matter tracts and wellbeing measures were found. These results provide preliminary evidence for potential neurobiological markers of mental health and wellbeing in early adolescence and should be tracked longitudinally to provide more detailed and robust findings.

The inferior fronto-occipital fasciculus correlates with early precursors of mathematics and reading before the start of formal schooling

Diffusion-weighted imaging studies in preschoolers have almost exclusively been done in the field of reading. As a result, virtually nothing is known about white matter tracts associated with individual differences in mathematics at this age. Studying the preschoolers' brain is crucial because it allows us to identify individual differences in brain anatomy without influences of formal mathematics and reading instruction. To fill this gap, we investigated for the first time before the start of formal school entry the associations between white matter tracts and precursors of mathematics and reading simultaneously. We also investigated whether these associations were specific to mathematics and to reading, or not. We focused on four bilateral white matter tracts (arcuate fasciculus (direct, anterior), inferior fronto-occipital fasciculus, inferior longitudinal fasciculus), which have been previously correlated with mathematical performance in older children and with reading performance in children of a similar age as the current study. Participants were 56 5-year-old children (Mage = 67 months; SD = 1.8), none of which received formal instruction. Our results showed an association between the bilateral inferior fronto-occipital fasciculus and precursors of mathematics (numerical ordering, numeral knowledge) and reading (phonological awareness, letter knowledge). Follow-up regression analyses revealed that the associations found with the inferior fronto-occipital fasciculus were neither specific to mathematics nor specific to reading. These findings suggest that, already before the start of formal schooling, the inferior fronto-occipital fasciculus might be related to the neural overlap between mathematics and reading. This overlap potentially reflects one of their many shared mechanisms, such as the reliance on phonological codes or the processing of visual symbols, and these mechanisms should be exploited in future studies.

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 ).

Physics-Informed Discretization for Reproducible and Robust Radiomic Feature Extraction Using Quantitative MRI

OBJECTIVE

Given the limited repeatability and reproducibility of radiomic features derived from weighted magnetic resonance imaging (MRI), there may be significant advantages to using radiomics in conjunction with quantitative MRI. This study introduces a novel physics-informed discretization (PID) method for reproducible radiomic feature extraction and evaluates its performance using quantitative MRI sequences including magnetic resonance fingerprinting (MRF) and apparent diffusion coefficient (ADC) mapping.

MATERIALS AND METHODS

A multiscanner, scan-rescan dataset comprising whole-brain 3D quantitative (MRF T1, MRF T2, and ADC) and weighted MRI (T1w MPRAGE, T2w SPACE, and T2w FLAIR) from 5 healthy subjects was prospectively acquired. Subjects underwent 2 repeated acquisitions on 3 distinct 3 T scanners each, for a total of 6 scans per subject (30 total scans). First-order statistical (n = 23) and second-order texture (n = 74) radiomic features were extracted from 56 brain tissue regions of interest using the proposed PID method (for quantitative MRI) and conventional fixed bin number (FBN) discretization (for quantitative MRI and weighted MRI). Interscanner radiomic feature reproducibility was measured using the intraclass correlation coefficient (ICC), and the effect of image sequence (eg, MRF T1 vs T1w MPRAGE), as well as image discretization method (ie, PID vs FBN), on radiomic feature reproducibility was assessed using repeated measures analysis of variance. The robustness of PID and FBN discretization to segmentation error was evaluated by simulating segmentation differences in brainstem regions of interest. Radiomic features with ICCs greater than 0.75 following simulated segmentation were determined to be robust to segmentation.

RESULTS

First-order features demonstrated higher reproducibility in quantitative MRI than weighted MRI sequences, with 30% (n = 7/23) features being more reproducible in MRF T1 and MRF T2 than weighted MRI. Gray level co-occurrence matrix (GLCM) texture features extracted from MRF T1 and MRF T2 were significantly more reproducible using PID compared with FBN discretization; for all quantitative MRI sequences, PID yielded the highest number of texture features with excellent reproducibility (ICC > 0.9). Comparing texture reproducibility of quantitative and weighted MRI, a greater proportion of MRF T1 (n = 225/370, 61%) and MRF T2 (n = 150/370, 41%) texture features had excellent reproducibility (ICC > 0.9) compared with T1w MPRAGE (n = 148/370, 40%), ADC (n = 115/370, 32%), T2w SPACE (n = 98/370, 27%), and FLAIR (n = 102/370, 28%). Physics-informed discretization was also more robust than FBN discretization to segmentation error, as 46% (n = 103/222, 46%) of texture features extracted from quantitative MRI using PID were robust to simulated 6 mm segmentation shift compared with 19% (n = 42/222, 19%) of weighted MRI texture features extracted using FBN discretization.

CONCLUSIONS

The proposed PID method yields radiomic features extracted from quantitative MRI sequences that are more reproducible and robust than radiomic features extracted from weighted MRI using conventional (FBN) discretization approaches. Quantitative MRI sequences also demonstrated greater scan-rescan robustness and first-order feature reproducibility than weighted MRI.

Diffusion time-related structure-function coupling reveals differential association with inter-individual variations in body mass index

Body mass index (BMI) is an indicator of obesity, and recent neuroimaging studies have demonstrated that inter-individual variations in BMI are associated with altered brain structure and function. However, the mechanism underlying the alteration of structure-function correspondence according to BMI is under-investigated. In this study, we studied structural and functional connectivity derived from diffusion MRI tractography and inter-regional correlations of functional MRI time series, respectively. We combined the structural and functional connectivity information using the Riemannian optimization approach. First, the low-dimensional principal eigenvectors (i.e., gradients) of the structural connectivity were generated by applying diffusion map embedding with varying diffusion times. A transformation was identified so that the structural and functional embeddings share the same coordinate system, and subsequently, the functional connectivity matrix was simulated. Then, we generated gradients from the simulated functional connectivity matrix. We found the most apparent cortical hierarchical organization differentiating between low-level sensory and higher-order transmodal regions in the middle of the diffusion time, indicating that the hierarchical organization of the brain may reflect the intermediate mechanisms of mono- and polysynaptic communications. Associations between the functional gradients and BMI were strongest when the hierarchical structure was the most evident. Moreover, the gradient-BMI association map was related to the microstructural features, and the findings indicated that the BMI-related structure-function coupling was significantly associated with brain microstructure, particularly in higher-order transmodal areas. Finally, transcriptomic association analysis revealed the potential biological underpinnings specifying gene enrichment in the striatum, hypothalamus, and cortical cells. Our findings provide evidence that structure-function correspondence is strongly coupled with BMI when hierarchical organization is the most apparent and that the associations are related to the multiscale properties of the brain, leading to an advanced understanding of the neural mechanisms related to BMI.

Vessel Wall Characterization Using Quantitative MR Imaging

MR imaging's exceptional capabilities in vascular imaging stem from its ability to visualize and quantify vessel wall features, such as plaque burden, composition, and biomechanical properties. The application of advanced MR imaging techniques, including two-dimensional and three-dimensional black-blood MR imaging, T1 and T2 relaxometry, diffusion-weighted imaging, and dynamic contrast-enhanced MR imaging, wall shear stress, and arterial stiffness, empowers clinicians and researchers to explore the intricacies of vascular diseases. This array of techniques provides comprehensive insights into the development and progression of vascular pathologies, facilitating earlier diagnosis, targeted treatment, and improved patient outcomes in the management of vascular health.

The Neurobiology of Life Course Socioeconomic Conditions and Associated Cognitive Performance in Middle to Late Adulthood

Despite major advances, our understanding of the neurobiology of life course socioeconomic conditions is still scarce. This study aimed to provide insight into the pathways linking socioeconomic exposures-household income, last known occupational position, and life course socioeconomic trajectories-with brain microstructure and cognitive performance in middle to late adulthood. We assessed socioeconomic conditions alongside quantitative relaxometry and diffusion-weighted magnetic resonance imaging indicators of brain tissue microstructure and cognitive performance in a sample of community-dwelling men and women (N = 751, aged 50-91 years). We adjusted the applied regression analyses and structural equation models for the linear and nonlinear effects of age, sex, education, cardiovascular risk factors, and the presence of depression, anxiety, and substance use disorders. Individuals from lower-income households showed signs of advanced brain white matter (WM) aging with greater mean diffusivity (MD), lower neurite density, lower myelination, and lower iron content. The association between household income and MD was mediated by neurite density (B = 0.084, p = 0.003) and myelination (B = 0.019, p = 0.009); MD partially mediated the association between household income and cognitive performance (B = 0.017, p < 0.05). Household income moderated the relation between WM microstructure and cognitive performance, such that greater MD, lower myelination, or lower neurite density was only associated with poorer cognitive performance among individuals from lower-income households. Individuals from higher-income households showed preserved cognitive performance even with greater MD, lower myelination, or lower neurite density. These findings provide novel mechanistic insights into the associations between socioeconomic conditions, brain anatomy, and cognitive performance in middle to late adulthood.

Significance of Persistent Systemic Support in the Clinical Course of Delayed Post-hypoxic Leukoencephalopathy Following Severe Coronavirus Disease 2019

A 45-year-old woman was hospitalized with severe coronavirus disease 2019 pneumonia. Following cytokine storm-induced multiorgan failure and lethal arrhythmia, the patient developed a sustained coma with flaccid quadriplegia. A cerebrospinal fluid examination excluded infectious and immunogenic encephalopathies, and diffusion-weighted magnetic resonance imaging demonstrated high-intensity areas in the white matter with a cortex-sparing distribution, suggesting delayed post-hypoxic leukoencephalopathy. As a result of intensive cardiopulmonary support for a month, the neurological function gradually recovered. Based on the reversible clinical course noted in this patient, accurate diagnosis and persistent medical approaches are important for the management of coronavirus disease 2019-related delayed post-hypoxic leukoencephalopathy.

Structural-functional brain network coupling predicts human cognitive ability

Individual differences in general cognitive ability (GCA) have a biological basis within the structure and function of the human brain. Network neuroscience investigations revealed neural correlates of GCA in structural as well as in functional brain networks. However, whether the relationship between structural and functional networks, the structural-functional brain network coupling (SC-FC coupling), is related to individual differences in GCA remains an open question. We used data from 1030 adults of the Human Connectome Project, derived structural connectivity from diffusion weighted imaging, functional connectivity from resting-state fMRI, and assessed GCA as a latent g-factor from 12 cognitive tasks. Two similarity measures and six communication measures were used to model possible functional interactions arising from structural brain networks. SC-FC coupling was estimated as the degree to which these measures align with the actual functional connectivity, providing insights into different neural communication strategies. At the whole-brain level, higher GCA was associated with higher SC-FC coupling, but only when considering path transitivity as neural communication strategy. Taking region-specific variations in the SC-FC coupling strategy into account and differentiating between positive and negative associations with GCA, allows for prediction of individual cognitive ability scores in a cross-validated prediction framework (correlation between predicted and observed scores: r = 0.25, p < .001). The same model also predicts GCA scores in a completely independent sample (N = 567, r = 0.19, p < .001). Our results propose structural-functional brain network coupling as a neurobiological correlate of GCA and suggest brain region-specific coupling strategies as neural basis of efficient information processing predictive of cognitive ability.

Refining the predictive value of preoperative apparent diffusion coefficient (ADC) by whole-tumor analysis for facial nerve outcomes in vestibular schwannomas

BACKGROUND

Apparent diffusion coefficient (ADC) in MRI has been shown to correlate with postoperative House-Brackmann (HB) scores in patients with vestibular schwannoma despite limited methodology. To rectify limitations of single region of interest (ROI) sampling, we hypothesize that whole-tumor ADC histogram analysis will refine the predictive value of this preoperative biomarker related to postoperative facial nerve function.

METHODS

Of 155 patients who underwent resection of vestibular schwannoma (2014-2020), 125 patients were included with requisite clinical and radiographic data. After volumetric analysis and whole-tumor ADC histogram, regression tree analysis identified ADC cutoff for significant differences in HB grade. Outcomes were extent of resection, facial nerve function, hospital length of stay (LOS), and complications.

RESULTS

Regression tree analysis defined three quantitative ADC groups (× 10-6 mm2/s) as high (> 2248.77; HB 1.7), mid (1468.44-2248.77; HB 3.1), and low (< 1468.44; HB 2.3) range (p 0.04). The mid-range ADC group had significantly worse postoperative HB scores and longer hospital LOS. Large tumor volume was independently predictive of lower rates of gross total resection (p <0.0001), higher postoperative HB score (p 0.002), higher rate of complications (p 0.04), and longer LOS (p 0.003).

CONCLUSIONS

Whole-tumor histogram yielded a robust regression tree analysis that defined three ADC groups with significantly different facial nerve outcomes. This likely reflects tumor heterogeneity better than solid-tumor ROI sampling. Whole-tumor ADC warrants further study as a useful radiographic biomarker in patients with vestibular schwannoma who are considering surgical resection.

White matter tracts adjacent to the human cingulate sulcus visual area (CSv)

Human cingulate sulcus visual area (CSv) was first identified as an area that responds selectively to visual stimulation indicative of self-motion. It was later shown that the area is also sensitive to vestibular stimulation as well as to bodily motion compatible with locomotion. Understanding the anatomical connections of CSv will shed light on how CSv interacts with other parts of the brain to perform information processing related to self-motion and navigation. A previous neuroimaging study (Smith et al. 2018, Cerebral Cortex, 28, 3685-3596) used diffusion-weighted magnetic resonance imaging (dMRI) to examine the structural connectivity of CSv, and demonstrated connections between CSv and the motor and sensorimotor areas in the anterior and posterior cingulate sulcus. The present study aimed to complement this work by investigating the relationship between CSv and adjacent major white matter tracts, and to map CSv's structural connectivity onto known white matter tracts. By re-analysing the dataset from Smith et al. (2018), we identified bundles of fibres (i.e. streamlines) from the whole-brain tractography that terminate near CSv. We then assessed to which white matter tracts those streamlines may belong based on previously established anatomical prescriptions. We found that a significant number of CSv streamlines can be categorised as part of the dorsalmost branch of the superior longitudinal fasciculus (SLF I) and the cingulum. Given current thinking about the functions of these white matter tracts, our results support the proposition that CSv provides an interface between sensory and motor systems in the context of self-motion.

Magnetic Resonance Imaging Assists With Determining Etiology After Transient Ischemic Attack or Minor Stroke

BACKGROUND

Magnetic resonance imaging infarct topography may assist with determining stroke etiology. The influence of diffusion-weighted imaging (DWI)-positive lesions on etiology determination in patients with transient ischemic attack or minor stroke is not well studied.

METHODS AND RESULTS

We prospectively enrolled patients between 2010 and 2017 in 2 studies; participants with a final diagnosis of probable or definite transient ischemic attack or stroke were pooled for analysis. The primary outcome was the adjudicated ischemic etiology. We compared proportion of each etiology (cardioembolic, large-vessel, small-vessel disease, other) in patients who had DWI positivity compared with DWI negativity. We used logistic regression to determine the adjusted odds ratio (OR) for each etiology compared with undetermined by DWI positivity. The final analysis included 1498 patients: 832 (55.5%) were DWI-positive. DWI-positive patients were more likely to be diagnosed with small-vessel disease (19.1% versus 5.3%) and less likely with undetermined etiology (36.9% versus 53.0%; P<0.001). After adjustment, the presence of any DWI lesion was associated with increased odds of assigning any etiology (OR, 1.8 [95% CI, 1.3-2.5]). A single DWI lesion was associated with increased odds of small-vessel disease diagnosis (OR, 9.5 [95% CI, 6.4-14.0]), and multiple DWI lesions with reduced odds of small-vessel disease (OR, 0.2 [95% CI, 0.1-0.4]) but increased odds of all other etiologies compared with undetermined etiology.

CONCLUSIONS

Any DWI-positive lesion after suspected transient ischemic attack or minor stroke was associated with increased odds of assigning a etiology. Presence and topography of DWI lesions on magnetic resonance imaging may assist with etiology determination and may impact stroke prevention therapies.

Diffusion tensor imaging in anisotropic tissues: application of reduced gradient vector schemes in peripheral nerves

BACKGROUND

In contrast to the brain, fibers within peripheral nerves have distinct monodirectional structure questioning the necessity of complex multidirectional gradient vector schemes for DTI. This proof-of-concept study investigated the diagnostic utility of reduced gradient vector schemes in peripheral nerve DTI.

METHODS

Three-Tesla magnetic resonance neurography of the tibial nerve using 20-vector DTI (DTI20) was performed in 10 healthy volunteers, 12 patients with type 2 diabetes, and 12 age-matched healthy controls. From the full DTI20 dataset, three reduced datasets including only two or three vectors along the x- and/or y- and z-axes were built to calculate major parameters. The influence of nerve angulation and intraneural connective tissue was assessed. The area under the receiver operating characteristics curve (ROC-AUC) was used for analysis.

RESULTS

Simplified datasets achieved excellent diagnostic accuracy equal to DTI20 (ROC-AUC 0.847-0.868, p ≤ 0.005), but compared to DTI20, the reduced models yielded mostly lower absolute values of DTI scalars: median fractional anisotropy (FA) ≤ 0.12; apparent diffusion coefficient (ADC) ≤ 0.25; axial diffusivity ≤ 0.96, radial diffusivity ≤ 0.07). The precision of FA and ADC with the three-vector model was closest to DTI20. Intraneural connective tissue was negatively correlated with FA and ADC (r ≥ -0.49, p < 0.001). Small deviations of nerve angulation had little effect on FA accuracy.

CONCLUSIONS

In peripheral nerves, bulk tissue DTI metrics can be approximated with only three predefined gradient vectors along the scanner's main axes, yielding similar diagnostic accuracy as a 20-vector DTI, resulting in substantial scan time reduction.

RELEVANCE STATEMENT

DTI bulk tissue parameters of peripheral nerves can be calculated with only three predefined gradient vectors at similar diagnostic performance as a standard DTI but providing a substantial scan time reduction.

KEY POINTS

• In peripheral nerves, DTI parameters can be approximated using only three gradient vectors. • The simplified model achieves a similar diagnostic performance as a standard DTI. • The simplified model allows for a significant acceleration of image acquisition. • This can help to introduce multi-b-value DTI techniques into clinical practice.

Does Bladder Cancer Subtype Influence Pathologic Complete Response (pCR) and Pelvic Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) Response Evaluation After Neoadjuvant Chemotherapy? Pathological Perspective

INTRODUCTION

We aimed to provide a pathological perspective on the management of muscle-invasive bladder cancer (MIBC) by correlating the prechemotherapy transurethral resection of bladder tumor findings and postchemotherapy radiologic evaluation with final radical cystectomy (RC) findings.

MATERIALS AND METHODS

This retrospective study included 79 MIBC patients treated with neoadjuvant chemotherapy (NAC) and RC. Pelvic diffusion-weighted magnetic resonance imaging (DW-MRI) and pathologic reports were retrieved from our institutional database. All pathology slides were reviewed based on diagnostic criteria with high interobserver reproducibility.

RESULTS

Pathologic complete response (pCR) was confirmed in 32 patients (40.5%). The concordance and discordance between MRI and RC findings occurred in 68.3% and 31.7% of cases, respectively. The 21.5% of cases that were clinical CR (cCR) on MRI actually achieved pCR on RC specimens and 46.8% of cases that were non-cCR on MRI were actually non-pCR on RC specimens. In 19.0% of cases, RC findings were pCR, but MRI demonstrated residual tumor and the opposite was 12.7%. The greatest discrepancy between the 2 methods (75%, 3/4) was for the plasmacytoid subtype. Plasmacytoid histology was the most common histological subtype identified in RC specimens after NAC, followed by micropapillary and squamous histologies.

CONCLUSIONS

We found that all cases with plasmacytoid and micropapillary subtypes, and squamous differentiation did not show pCR. In particular, the largest discrepancy between MRI findings and RC pathology after NAC was seen in the plasmacytoid subtype. An accurate pathologic diagnosis based on strict criteria to identify histological subtypes of MIBC is necessary for proper treatment.

White and Gray Matter Abnormality in Burning Mouth Syndrome Evaluated with Diffusion Tensor Imaging and Neurite Orientation Dispersion and Density Imaging

PURPOSE

Burning mouth syndrome (BMS) is defined by a burning sensation or pain in the tongue or other oral sites despite the presence of normal mucosa on inspection. Both psychiatric and neuroimaging investigations have examined BMS; however, there have been no analyses using the neurite orientation dispersion and density imaging (NODDI) model, which provides detailed information of intra- and extracellular microstructures. Therefore, we performed voxel-wise analyses using both NODDI and diffusion tensor imaging (DTI) models and compared the results to better comprehend the pathology of BMS.

METHODS

Fourteen patients with BMS and 11 age- and sex-matched healthy control subjects were prospectively scanned using a 3T-MRI machine using 2-shell diffusion imaging. Diffusion tensor metrics (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD], and radial diffusivity [RD]) and neurite orientation and dispersion index metrics (intracellular volume fraction [ICVF], isotropic volume fraction [ISO], and orientation dispersion index [ODI]) were retrieved from diffusion MRI data. These data were analyzed using tract-based spatial statistics (TBSS) and gray matter-based spatial statistics (GBSS).

RESULTS

TBSS analysis showed that patients with BMS had significantly higher FA and ICVF and lower MD and RD than the healthy control subjects (family-wise error [FWE] corrected P < 0.05). Changes in ICVF, MD, and RD were observed in widespread white matter areas. Fairly small areas with different FA were included. GBSS analysis showed that patients with BMS had significantly higher ISO and lower MD and RD than the healthy control subjects (FWE-corrected P < 0.05), mainly limited to the amygdala.

CONCLUSION

The increased ICVF in the BMS group may represent myelination and/or astrocytic hypertrophy, and microstructural changes in the amygdala in GBSS analysis indicate the emotional-affective profile of BMS.

Smooth muscle tumours of the uterus: MR imaging malignant predictive features-a 12-year analysis in a referral hospital in Portugal

PURPOSE

To evaluate the magnetic resonance imaging (MRI) features that may help distinguish leiomyosarcomas from atypical leiomyomas (those presenting hyperintensity on T2-W images equal or superior to 50% compared to the myometrium).

MATERIALS AND METHODS

The authors conducted a retrospective single-centre study that included a total of 57 women diagnosed with smooth muscle tumour of the uterus, who were evaluated with pelvic MRI, between January 2009 and March 2020. All cases had a histologically proven diagnosis (31 Atypical Leiomyomas-ALM; 26 Leiomyosarcomas-LMS). The MRI features evaluated in this study included: age at presentation, dimension, contours, intra-tumoral haemorrhagic areas, T2-WI heterogeneity, T2-WI dark areas, flow voids, cyst areas, necrosis, restriction on diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) values, signal intensity and heterogeneity after contrast administration in T1-WI, presence and location of unenhanced areas. The association between the MRI characteristics and the histological subtype was evaluated using Chi-Square and ANOVA tests.

RESULTS

The MRI parameters that showed a statistically significance correlation with malignant histology and thus most strongly associated with LMS were found to be: irregular contours (p < 0.001), intra-tumoral haemorrhagic areas (p = 0.028), T2-WI dark areas (p = 0.016), high signal intensity after contrast administration (p = 0.005), necrosis (p = 0.001), central location for unenhanced areas (p = 0.026), and ADC value lower than 0.88 × 10-3 mm2/s (p = 0.002).

CONCLUSION

With our work, we demonstrate the presence of seven MRI features that are statistically significant in differentiating between LMS and ALM.

Discrimination between human epidermal growth factor receptor 2 (HER2)-low-expressing and HER2-overexpressing breast cancers: a comparative study of four MRI diffusion models

OBJECTIVES

To determine the value of conventional DWI, continuous-time random walk (CTRW), fractional order calculus (FROC), and stretched exponential model (SEM) in discriminating human epidermal growth factor receptor 2 (HER2) status of breast cancer (BC).

METHODS

This prospective study included 158 women who underwent DWI, CTRW, FROC, and SEM and were pathologically categorized into the HER2-zero-expressing group (n = 10), HER2-low-expressing group (n = 86), and HER2-overexpressing group (n = 62). Nine diffusion parameters, namely ADC, αCTRW, βCTRW, DCTRW, βFROC, DFROC, μFROC, αSEM, and DDCSEM of the primary tumor, were derived from four diffusion models. These diffusion metrics and clinicopathologic features were compared between groups. Logistic regression was used to determine the optimal diffusion metrics and clinicopathologic variables for classifying the HER2-expressing statuses. Receiver operating characteristic (ROC) curves were used to evaluate their discriminative ability.

RESULTS

The estrogen receptor (ER) status, progesterone receptor (PR) status, and tumor size differed between HER2-low-expressing and HER2-overexpressing groups (p < 0.001 to p = 0.009). The αCTRW, DCTRW, βFROC, DFROC, μFROC, αSEM, and DDCSEM were significantly lower in HER2-low-expressing BCs than those in HER2-overexpressing BCs (p < 0.001 to p = 0.01). Further multivariable logistic regression analysis showed that the αCTRW was the single best discriminative metric, with an area under the curve (AUC) being higher than that of ADC (0.802 vs. 0.610, p < 0.05); the addition of ER status, PR status, and tumor size to the αCTRW improved the AUC to 0.877.

CONCLUSIONS

The αCTRW could help discriminate the HER2-low-expressing and HER2-overexpressing BCs.

CLINICAL RELEVANCE STATEMENT

Human epidermal growth factor receptor 2 (HER2)-low-expressing breast cancer (BC) might also benefit from the HER2-targeted therapy. Prediction of HER2-low-expressing BC or HER2-overexpressing BC is crucial for appropriate management. Advanced continuous-time random walk diffusion MRI offers a solution to this clinical issue.

KEY POINTS

• Human epidermal receptor 2 (HER2)-low-expressing BC had lower αCTRW, DCTRW, βFROC, DFROC, μFROC, αSEM, and DDCSEM values compared with HER2-overexpressing breast cancer. • The αCTRW was the single best diffusion metric (AUC = 0.802) for discrimination between the HER2-low-expressing and HER2-overexpressing breast cancers. • The addition of αCTRW to the clinicopathologic features (estrogen receptor status, progesterone receptor status, and tumor size) further improved the discriminative ability.

Diffusion MRI relates to plasma Aβ42/40 in PET negative participants without dementia

INTRODUCTION

Magnetic resonance imaging (MRI) biomarkers are needed for indexing early biological stages of Alzheimer's disease (AD), such as plasma amyloid-β (Aβ42/40) positivity in Aβ positron emission tomography (PET) negative individuals.

METHODS

Diffusion free-water (FW) MRI was acquired in individuals with normal cognition (NC) and mild cognitive impairment (MCI) with Aβ plasma-/PET- (NC = 22, MCI = 60), plasma+/PET- (NC = 5, MCI = 20), and plasma+/PET+ (AD dementia = 21) biomarker status. Gray and white matter FW and fractional anisotropy (FAt) were compared cross-sectionally and the relationships between imaging, plasma and PET biomarkers were assessed.

RESULTS

Plasma+/PET- demonstrated increased FW (24 regions) and decreased FAt (66 regions) compared to plasma-/PET-. FW (16 regions) and FAt (51 regions) were increased in plasma+/PET+ compared to plasma+/PET-. Composite brain FW correlated with plasma Aβ42/40 and p-tau181.

DISCUSSION

FW imaging changes distinguish plasma Aβ42/40 positive and negative groups, independent of group differences in cognitive status, Aβ PET status, and other plasma biomarkers (i.e., t-tau, p-tau181, glial fibrillary acidic protein, neurofilament light).

HIGHLIGHTS

Plasma Aβ42/40 positivity is associated with brain microstructure decline. Plasma+/PET- demonstrated increased FW in 24 total GM and WM regions. Plasma+/PET- demonstrated decreased FAt in 66 total GM and WM regions. Whole-brain FW correlated with plasma Aβ42/40 and p-tau181 measures. Plasma+/PET- demonstrated decreased cortical volume and thickness.

Diffusion MRI marks progressive alterations in fiber integrity in the zQ175DN mouse model of Huntington's disease

Huntington's disease (HD) is a progressive neurodegenerative disease affecting motor and cognitive abilities. Multiple studies have found white matter anomalies in HD-affected humans and animal models of HD. The identification of sensitive white-matter-based biomarkers in HD animal models will be important in understanding disease mechanisms and testing the efficacy of therapeutic interventions. Here we investigated the progression of white matter deficits in the knock-in zQ175DN heterozygous (HET) mouse model of HD at 3, 6 and 11 months of age (M), reflecting different states of phenotypic progression. We compared findings from traditional diffusion tensor imaging (DTI) and advanced fixel-based analysis (FBA) diffusion metrics for their sensitivity in detecting white matter anomalies in the striatum, motor cortex, and segments of the corpus callosum. FBA metrics revealed progressive and widespread reductions of fiber cross-section and fiber density in myelinated bundles of HET mice. The corpus callosum genu was the most affected structure in HET mice at 6 and 11 M based on the DTI and FBA metrics, while the striatum showed the earliest progressive differences starting at 3 M based on the FBA metrics. Overall, FBA metrics detected earlier and more prominent alterations in myelinated fiber bundles compared to the DTI metrics. Luxol fast blue staining showed no loss in myelin density, indicating that diffusion anomalies could not be explained by myelin reduction but diffusion anomalies in HET mice were accompanied by increased levels of neurofilament light chain protein at 11 M. Altogether, our findings reveal progressive alterations in myelinated fiber bundles that can be measured using diffusion MRI, representing a candidate noninvasive imaging biomarker to study phenotype progression and the efficacy of therapeutic interventions in zQ175DN mice. Moreover, our study exposed higher sensitivity of FBA than DTI metrics, suggesting a potential benefit of adopting these advanced metrics in other contexts, including biomarker development in humans.

Cortical-cerebellar circuits changes in preschool ASD children by multimodal MRI

OBJECTIVE

To investigate the alterations in cortical-cerebellar circuits and assess their diagnostic potential in preschool children with autism spectrum disorder using multimodal magnetic resonance imaging.

METHODS

We utilized diffusion basis spectrum imaging approaches, namely DBSI_20 and DBSI_combine, alongside 3D structural imaging to examine 31 autism spectrum disorder diagnosed patients and 30 healthy controls. The participants' brains were segmented into 120 anatomical regions for this analysis, and a multimodal strategy was adopted to assess the brain networks using a multi-kernel support vector machine for classification.

RESULTS

The results revealed consensus connections in the cortical-cerebellar and subcortical-cerebellar circuits, notably in the thalamus and basal ganglia. These connections were predominantly positive in the frontoparietal and subcortical pathways, whereas negative consensus connections were mainly observed in frontotemporal and subcortical pathways. Among the models tested, DBSI_20 showed the highest accuracy rate of 86.88%. In addition, further analysis indicated that combining the 3 models resulted in the most effective performance.

CONCLUSION

The connectivity network analysis of the multimodal brain data identified significant abnormalities in the cortical-cerebellar circuits in autism spectrum disorder patients. The DBSI_20 model not only provided the highest accuracy but also demonstrated efficiency, suggesting its potential for clinical application in autism spectrum disorder diagnosis.

Editorial for "Problem Solving MRI to Reduce False-Positive Biopsy Related to Breast US: Conductivity vs. DWI vs. Abbreviated Contrast-Enhanced MRI"

Level of Evidence

5

Technical Efficacy Stage

3

Isolated infantile myofibroma of the calvarium: Report of a case with a literature review

OBJECTIVE

Infantile myofibromatosis is a rare entity of childhood characterized by benign myofibroblastic tumors in the soft tissues, the bones, and occasionally the viscera. Solitary skeletal lesions are relatively uncommon. Calvarial involvement should be distinguished from more aggressive tumors for appropriate treatment.

METHODS

We reviewed solitary infantile myofibroma of the calvarium and discussed the relevant computed tomography and magnetic resonance imaging findings along with differential diagnosis. A case study of the frontal bone in a 5-month-old girl was also presented.

RESULTS

Fourteen cases were reviewed, including the current case. Of the 13 cases with known sex, eight were male and five female. The mean age was 3.03 with an age range of 0.41-9 years. Nine of the 14 tumors were in the frontal bone. The lesions were intradiploic with tabula interna and/or externa of the calvaria involvement. The mean largest diameter was 22.3 mm. Upon computed tomography, all the lesions were expansile and lytic, and hypoattenuated, isoattenuated or occasionally hyperatenuated. Calcification was not seen. On magnetic resonance imaging, most neoplasms were hypointense on T1-weighted and T2-weighted images. Neoplasms showed hypointense signal on diffusion-weighted imaging and hyperintense on apparent diffusion coefficient, without restricted diffusion in three cases. All lesions were intensely enhanced after gadolinium administration. Treatment was total surgical resection and recurrence was not observed during follow-up.

CONCLUSIONS

Infantile myofibromas are rare, typically intradiploic expansile lytic lesions with tabula interna and/or externa involvement. Distinctive imaging features include the presence of hipointense signals on T2-weighted magnetic resonance images without restricted diffusion on diffusion-weighted imaging. A slow-growing, firm, painless, and nontender mass with supportive imaging findings should raise suspicion of the disease.

Neural orientation distribution fields for estimation and uncertainty quantification in diffusion MRI

Inferring brain connectivity and structure in-vivo requires accurate estimation of the orientation distribution function (ODF), which encodes key local tissue properties. However, estimating the ODF from diffusion MRI (dMRI) signals is a challenging inverse problem due to obstacles such as significant noise, high-dimensional parameter spaces, and sparse angular measurements. In this paper, we address these challenges by proposing a novel deep-learning based methodology for continuous estimation and uncertainty quantification of the spatially varying ODF field. We use a neural field (NF) to parameterize a random series representation of the latent ODFs, implicitly modeling the often ignored but valuable spatial correlation structures in the data, and thereby improving efficiency in sparse and noisy regimes. An analytic approximation to the posterior predictive distribution is derived which can be used to quantify the uncertainty in the ODF estimate at any spatial location, avoiding the need for expensive resampling-based approaches that are typically employed for this purpose. We present empirical evaluations on both synthetic and real in-vivo diffusion data, demonstrating the advantages of our method over existing approaches.

Gray and White Matter Metrics Demonstrate Distinct and Complementary Prediction of Differences in Cognitive Performance in Children: Findings from ABCD (N = 11,876)

Individual differences in cognitive performance in childhood are a key predictor of significant life outcomes such as educational attainment and mental health. Differences in cognitive ability are governed in part by variations in brain structure. However, studies commonly focus on either gray or white matter metrics in humans, leaving open the key question as to whether gray or white matter microstructure plays distinct or complementary roles supporting cognitive performance. To compare the role of gray and white matter in supporting cognitive performance, we used regularized structural equation models to predict cognitive performance with gray and white matter measures. Specifically, we compared how gray matter (volume, cortical thickness, and surface area) and white matter measures (volume, fractional anisotropy, and mean diffusivity) predicted individual differences in cognitive performance. The models were tested in 11,876 children (ABCD Study; 5,680 female, 6,196 male) at 10 years old. We found that gray and white matter metrics bring partly nonoverlapping information to predict cognitive performance. The models with only gray or white matter explained respectively 15.4 and 12.4% of the variance in cognitive performance, while the combined model explained 19.0%. Zooming in, we additionally found that different metrics within gray and white matter had different predictive power and that the tracts/regions that were most predictive of cognitive performance differed across metrics. These results show that studies focusing on a single metric in either gray or white matter to study the link between brain structure and cognitive performance are missing a key part of the equation.

Diffusion-weighted imaging-based radiomics model using automatic machine learning to differentiate cerebral cystic metastases from brain abscesses

OBJECTIVES

To develop a radiomics model based on diffusion-weighted imaging (DWI) utilizing automated machine learning method to differentiate cerebral cystic metastases from brain abscesses.

MATERIALS AND METHODS

A total of 186 patients with cerebral cystic metastases (n = 98) and brain abscesses (n = 88) from two clinical institutions were retrospectively included. The datasets (129 from institution A) were randomly portioned into separate 75% training and 25% internal testing sets. Radiomics features were extracted from DWI images using two subregions of the lesion (cystic core and solid wall). A thorough image preprocessing method was applied to DWI images to ensure the robustness of radiomics features before feature extraction. Then the Tree-based Pipeline Optimization Tool (TPOT) was utilized to search for the best optimized machine learning pipeline, using a fivefold cross-validation in the training set. The external test set (57 from institution B) was used to evaluate the model's performance.

RESULTS

Seven distinct TPOT models were optimized to distinguish between cerebral cystic metastases and abscesses either based on different features combination or using wavelet transform. The optimal model demonstrated an AUC of 1.00, an accuracy of 0.97, sensitivity of 1.00, and specificity of 0.93 in the internal test set, based on the combination of cystic core and solid wall radiomics signature using wavelet transform. In the external test set, this model reached 1.00 AUC, 0.96 accuracy, 1.00 sensitivity, and 0.93 specificity.

CONCLUSION

The DWI-based radiomics model established by TPOT exhibits a promising predictive capacity in distinguishing cerebral cystic metastases from abscesses.

A noninvasive method for predicting clinically significant prostate cancer using magnetic resonance imaging combined with PRKY promoter methylation level: a machine learning study

BACKGROUND

Traditional process for clinically significant prostate cancer (csPCA) diagnosis relies on invasive biopsy and may bring pain and complications. Radiomic features of magnetic resonance imaging MRI and methylation of the PRKY promoter were found to be associated with prostate cancer.

METHODS

Fifty-four Patients who underwent prostate biopsy or photoselective vaporization of the prostate (PVP) from 2022 to 2023 were selected for this study, and their clinical data, blood samples and MRI images were obtained before the operation. Methylation level of two PRKY promoter sites, cg05618150 and cg05163709, were tested through bisulfite sequencing PCR (BSP). The PI-RADS score of each patient was estimated and the region of interest (ROI) was delineated by 2 experienced radiologists. After being extracted by a plug-in of 3D-slicer, radiomic features were selected through LASSCO regression and t-test. Selected radiomic features, methylation levels and clinical data were used for model construction through the random forest (RF) algorithm, and the predictive efficiency was analyzed by the area under the receiver operation characteristic (ROC) curve (AUC).

RESULTS

Methylation level of the site, cg05618150, was observed to be associated with prostate cancer, for which the AUC was 0.74. The AUC of T2WI in csPCA prediction was 0.84, which was higher than that of the apparent diffusion coefficient ADC (AUC = 0.81). The model combined with T2WI and clinical data reached an AUC of 0.94. The AUC of the T2WI-clinic-methylation-combined model was 0.97, which was greater than that of the model combined with the PI-RADS score, clinical data and PRKY promoter methylation levels (AUC = 0.86).

CONCLUSIONS

The model combining with radiomic features, clinical data and PRKY promoter methylation levels based on machine learning had high predictive efficiency in csPCA diagnosis.

Diffusion tensor imaging of white-matter structural features of maltreating mothers and their associations with intergenerational chain of childhood abuse

Child abuse causes lifelong adverse outcomes for both physical and mental health, although many are resilient. Efforts to prevent this issue from the parental side require an understanding of the neurobiological basis that leads abusive parents to perpetrate abuse and the influence of the intergenerational chain of childhood abuse. Therefore, this study was conducted to compare the brain white-matter fiber structures between 11 maltreating mothers who had been recognized as having conducted child abuse prior to the intervention and 40 age-matched control mothers using tract-based spatial statistics. There was a significantly reduced axial diffusivity (AD) and a similar trend in fractional anisotropy (FA) in the right corticospinal tract in maltreating mothers compared to control mothers. Therefore, maltreating mothers may have excessive control over the forcefulness of voluntary movements. These features also decreased as the number of childhood abuse experiences increased, suggesting that an intergenerational chain of child abuse may also be involved. Other aspects observed were that the higher the current depressive symptoms, the lower the AD and FA values; however, they were not related to parental practice or empathy. These results corroborate the neurobiological features that perpetrate behaviors in abusive mothers.

Thin-slice reverse encoding distortion correction DWI facilitates visualization of non-functioning pituitary neuroendocrine tumor (PitNET)/pituitary adenoma and surrounding normal structures

BACKGROUND

To evaluate the clinical usefulness of thin-slice echo-planar imaging (EPI)-based diffusion-weighted imaging (DWI) with an on-console distortion correction technique, termed reverse encoding distortion correction DWI (RDC-DWI), in patients with non-functioning pituitary neuroendocrine tumor (PitNET)/pituitary adenoma.

METHODS

Patients with non-functioning PitNET/pituitary adenoma who underwent 3-T RDC-DWI between December 2021 and September 2022 were retrospectively enrolled. Image quality was compared among RDC-DWI, DWI with correction for distortion induced by B0 inhomogeneity alone (B0-corrected-DWI), and original EPI-based DWI with anterior-posterior phase-encoding direction (AP-DWI). Susceptibility artifact, anatomical visualization of cranial nerves, overall tumor visualization, and visualization of cavernous sinus invasion were assessed qualitatively. Quantitative assessment of geometric distortion was performed by evaluation of anterior and posterior displacement between each DWI and the corresponding three-dimensional T2-weighted imaging. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient values were measured.

RESULTS

Sixty-four patients (age 70.8 ± 9.9 years [mean ± standard deviation]; 33 females) with non-functioning PitNET/pituitary adenoma were evaluated. In terms of susceptibility artifacts in the frontal and temporal lobes, visualization of left trigeminal nerve, overall tumor visualization, and anterior displacement, RDC-DWI performed the best and B0-corrected-DWI performed better than AP-DWI. The right oculomotor and right trigeminal nerves were better visualized by RDC-DWI than by B0-corrected-DWI and AP-DWI. Visualization of cavernous sinus invasion and posterior displacement were better by RDC-DWI and B0-corrected-DWI than by AP-DWI. SNR and CNR were the highest for RDC-DWI.

CONCLUSIONS

RDC-DWI achieved excellent image quality regarding susceptibility artifact, geometric distortion, and tumor visualization in patients with non-functioning PitNET/pituitary adenoma.

RELEVANCE STATEMENT

RDC-DWI facilitates excellent visualization of the pituitary region and surrounding normal structures, and its on-console distortion correction technique is convenient. RDC-DWI can clearly depict cavernous sinus invasion of PitNET/pituitary adenoma even without contrast medium.

KEY POINTS

• RDC-DWI is an EPI-based DWI technique with a novel on-console distortion correction technique. • RDC-DWI corrects distortion due to B0 field inhomogeneity and eddy current. • We evaluated the usefulness of thin-slice RDC-DWI in non-functioning PitNET/pituitary adenoma. • RDC-DWI exhibited excellent visualization in the pituitary region and surrounding structures. • In addition, the on-console distortion correction of RDC-DWI is clinically convenient.

Repeatability quantification of brain diffusion-weighted imaging for future clinical implementation at a low-field MR-linac

BACKGROUND

Longitudinal assessments of apparent diffusion coefficients (ADCs) derived from diffusion-weighted imaging (DWI) during intracranial radiotherapy at magnetic resonance imaging-guided linear accelerators (MR-linacs) could enable early response assessment by tracking tumor diffusivity changes. However, DWI pulse sequences are currently unavailable in clinical practice at low-field MR-linacs. Quantifying the in vivo repeatability of ADC measurements is a crucial step towards clinical implementation of DWI sequences but has not yet been reported on for low-field MR-linacs. This study assessed ADC measurement repeatability in a phantom and in vivo at a 0.35 T MR-linac.

METHODS

Eleven volunteers and a diffusion phantom were imaged on a 0.35 T MR-linac. Two echo-planar imaging DWI sequence variants, emphasizing high spatial resolution ("highRes") and signal-to-noise ratio ("highSNR"), were investigated. A test-retest study with an intermediate outside-scanner-break was performed to assess repeatability in the phantom and volunteers' brains. Mean ADCs within phantom vials, cerebrospinal fluid (CSF), and four brain tissue regions were compared to literature values. Absolute relative differences of mean ADCs in pre- and post-break scans were calculated for the diffusion phantom, and repeatability coefficients (RC) and relative RC (relRC) with 95% confidence intervals were determined for each region-of-interest (ROI) in volunteers.

RESULTS

Both DWI sequence variants demonstrated high repeatability, with absolute relative deviations below 1% for water, dimethyl sulfoxide, and polyethylene glycol in the diffusion phantom. RelRCs were 7% [5%, 12%] (CSF; highRes), 12% [9%, 22%] (CSF; highSNR), 9% [8%, 12%] (brain tissue ROIs; highRes), and 6% [5%, 7%] (brain tissue ROIs; highSNR), respectively. ADCs measured with the highSNR variant were consistent with literature values for volunteers, while smaller mean values were measured for the diffusion phantom. Conversely, the highRes variant underestimated ADCs compared to literature values, indicating systematic deviations.

CONCLUSIONS

High repeatability of ADC measurements in a diffusion phantom and volunteers' brains were measured at a low-field MR-linac. The highSNR variant outperformed the highRes variant in accuracy and repeatability, at the expense of an approximately doubled voxel volume. The observed high in vivo repeatability confirms the potential utility of DWI at low-field MR-linacs for early treatment response assessment.

Diffusion MRI tracks cortical microstructural changes during the early stages of Alzheimer's disease

There is increased interest in developing markers reflecting microstructural changes that could serve as outcome measures in clinical trials. This is especially important after unexpected results in trials evaluating disease-modifying therapies targeting amyloid-β (Aβ), where morphological metrics from MRI showed increased volume loss despite promising clinical treatment effects. In this study, changes over time in cortical mean diffusivity, derived using diffusion tensor imaging, were investigated in a large cohort (n = 424) of non-demented participants from the Swedish BioFINDER study. Participants were stratified following the Aβ/tau (AT) framework. The results revealed a widespread increase in mean diffusivity over time, including both temporal and parietal cortical regions, in Aβ-positive but still tau-negative individuals. These increases were steeper in Aβ-positive and tau-positive individuals and robust to the inclusion of cortical thickness in the model. A steeper increase in mean diffusivity was also associated with both changes over time in fluid markers reflecting astrocytic activity (i.e. plasma level of glial fibrillary acidic protein and CSF levels of YKL-40) and worsening of cognitive performance (all P < 0.01). By tracking cortical microstructural changes over time and possibly reflecting variations related to the astrocytic response, cortical mean diffusivity emerges as a promising marker for tracking treatments-induced microstructural changes in clinical trials.

Neuroscience meets behavior: A systematic literature review on magnetic resonance imaging of the brain combined with real-world digital phenotyping

A primary goal of neuroscience is to understand the relationship between the brain and behavior. While magnetic resonance imaging (MRI) examines brain structure and function under controlled conditions, digital phenotyping via portable automatic devices (PAD) quantifies behavior in real-world settings. Combining these two technologies may bridge the gap between brain imaging, physiology, and real-time behavior, enhancing the generalizability of laboratory and clinical findings. However, the use of MRI and data from PADs outside the MRI scanner remains underexplored. Herein, we present a Preferred Reporting Items for Systematic Reviews and Meta-Analysis systematic literature review that identifies and analyzes the current state of research on the integration of brain MRI and PADs. PubMed and Scopus were automatically searched using keywords covering various MRI techniques and PADs. Abstracts were screened to only include articles that collected MRI brain data and PAD data outside the laboratory environment. Full-text screening was then conducted to ensure included articles combined quantitative data from MRI with data from PADs, yielding 94 selected papers for a total of N = 14,778 subjects. Results were reported as cross-frequency tables between brain imaging and behavior sampling methods and patterns were identified through network analysis. Furthermore, brain maps reported in the studies were synthesized according to the measurement modalities that were used. Results demonstrate the feasibility of integrating MRI and PADs across various study designs, patient and control populations, and age groups. The majority of published literature combines functional, T1-weighted, and diffusion weighted MRI with physical activity sensors, ecological momentary assessment via PADs, and sleep. The literature further highlights specific brain regions frequently correlated with distinct MRI-PAD combinations. These combinations enable in-depth studies on how physiology, brain function and behavior influence each other. Our review highlights the potential for constructing brain-behavior models that extend beyond the scanner and into real-world contexts.

Quantitative diffusion imaging and genotype-by-sex interactions in a rat model of Alexander disease

PURPOSE

The clinical diagnosis and classification of Alexander disease (AxD) relies in part on qualitative neuroimaging biomarkers; however, these biomarkers fail to distinguish and discriminate different subtypes of AxD, especially in the presence of overlap in clinical symptoms. To address this gap in knowledge, we applied neurite orientation dispersion and density imaging (NODDI) to an innovative CRISPR-Cas9 rat genetic model of AxD to gain quantitative insights into the neural substrates and brain microstructural changes seen in AxD and to potentially identify novel quantitative NODDI biomarkers of AxD.

METHODS

Multi-shell DWI of age- and sex-matched AxD and wild-type Sprague Dawley rats (n = 6 per sex per genotype) was performed and DTI and NODDI measures calculated. A 3 × 2 × 2 analysis of variance model was used to determine the effect of genotype, biological sex, and laterality on quantitative measures of DTI and NODDI across regions of interest implicated in AxD.

RESULTS

There is a significant effect of genotype in the amygdala, hippocampus, neocortex, and thalamus in measures of both DTI and NODDI brain microstructure. A genotype by biological sex interaction was identified in DTI and NODDI measures in the corpus callosum, hippocampus, and neocortex.

CONCLUSION

We present the first application of NODDI to the study of AxD using a rat genetic model of AxD. Our analysis identifies alterations in NODDI and DTI measures to large white matter tracts and subcortical gray nuclei. We further identified genotype by sex interactions, suggesting a possible role for biological sex in the neuropathogenesis of AxD.