White Matter Unsupervised Analysis

What This Analysis Shows

We analyzed brain scans from 363 patients using diffusion tensor imaging (DTI), a technique that measures the structural integrity of white matter - the wiring that connects different brain regions. Without using any clinical diagnoses or labels, we evaluated each patient's white matter health against objective, published scientific standards.

131 patients (36%) show clinically significant white matter abnormality - patterns of structural damage consistent with what the peer-reviewed literature describes in traumatic brain injury.

The remaining 232 patients fall within normal or borderline ranges.

The Claim

These 131 patients exhibit measurable, objective white matter abnormalities that:

Are statistically significant - their brain metrics deviate beyond what would be expected by chance, after accounting for age, sex, and scanner differences
Follow the published pattern of brain injury - the specific tracts affected (corpus callosum, uncinate fasciculus, fornix, cingulum) are the same tracts identified across hundreds of peer-reviewed TBI studies
Are independent of subjective symptom reports - the abnormalities are detected from the imaging data alone, without reliance on patient self-report or clinical diagnosis
Show a dose-response relationship - patients categorized as more severely abnormal have progressively lower FA values across every major white matter tract, consistent with increasing degrees of structural damage

How Patients Were Evaluated

Each patient's brain scan was processed through DSI Studio (Yeh 2025, Nature Methods), which reconstructs 30 major white matter pathways and measures their structural properties. Three key measurements were used:

Fractional Anisotropy (FA) - measures how organized the white matter fibers are. Healthy fibers have high FA. Damaged fibers have low FA. This is the most widely used DTI metric in brain injury research.
Mean Diffusivity (MD) - measures how freely water moves through the tissue. Damaged tissue allows more water movement, producing higher MD.
Radial Diffusivity (RD) - measures water movement perpendicular to the fiber direction. Increases specifically with demyelination - damage to the insulating coating around nerve fibers.

These directions of change (FA decreasing, MD and RD increasing with injury) are established in the foundational work of Song et al. (2002, 2005) and confirmed across hundreds of subsequent studies.

Each patient was compared to other patients scanned on the same MRI machine, adjusted for age and sex, so that any detected abnormality reflects genuine deviation - not differences between scanners or normal aging.

A finding is flagged as abnormal when a patient's metric falls in the most extreme 5% in the injury direction (p < 0.05). A finding is flagged as severe when it falls in the most extreme 1% (p < 0.01). With approximately 60 measurements per patient, we expect about 3 false positives by chance. Patients classified as abnormal have findings well above this chance threshold.

Patient Groups

Normal - 95 patients (26%)

These patients' white matter falls within expected ranges. On average, fewer than 1 abnormal finding per patient - consistent with chance variation. No evidence of structural white matter injury from imaging.

Full list: filter subject_wm_health.csv for category = normal

Mild Abnormality - 137 patients (38%)

These patients show 1–2 findings above chance expectation, or had a white matter tract fail to reconstruct that typically succeeds at their scanner site. The abnormalities are minor and could reflect early or subtle changes. Most commonly affected structures are the fornix (memory circuit) and corpus callosum tapetum.

Full list: filter subject_wm_health.csv for category = mild_abnormality

Moderate Abnormality - 42 patients (12%)

These patients show a clear pattern of white matter abnormality - on average 4.7 abnormal findings per patient, with 1.5 reaching the severe threshold (p < 0.01). The most commonly affected tracts are:

Corpus callosum tapetum (43% of this group) - connects the temporal and parietal lobes across hemispheres
Fornix (33%) - the primary white matter pathway of the hippocampal memory circuit
Cingulum (31%) - connects frontal and limbic regions, critical for emotional regulation

Individual findings for each patient: filter subject_tract_findings.csv by subject_id

Severe Abnormality - 89 patients (25%)

These patients show widespread, high-confidence white matter damage. On average:

12.3 abnormal findings per patient (chance expectation: ~3)
6.7 findings at p < 0.01 - less than 1% probability of occurring in normal white matter
2.4 tracts that failed to reconstruct despite being routinely detectable at their scanner site
White matter affected across multiple functional systems simultaneously

The most commonly damaged structures in this group are:

White matter tract	Patients affected	What it connects	Clinical relevance
Corpus callosum body	59/89 (66%)	Left and right motor/sensory cortex	Coordination, processing speed
Corpus callosum forceps major	58/89 (65%)	Left and right visual cortex	Visual processing
Inferior longitudinal fasciculus (left)	57/89 (64%)	Temporal and occipital lobes	Reading, visual recognition
Uncinate fasciculus (left)	56/89 (63%)	Frontal and temporal lobes	Emotional regulation, memory
Corpus callosum forceps minor	55/89 (62%)	Left and right prefrontal cortex	Executive function, decision-making

This pattern - corpus callosum involvement combined with bilateral uncinate, inferior longitudinal fasciculus, and superior longitudinal fasciculus damage - is the signature of diffuse axonal injury, the most common form of white matter damage in traumatic brain injury. It has been reported in 19 out of 25 DTI studies of TBI reviewed by Hulkower et al. (2013) in the American Journal of Neuroradiology, and confirmed in meta-analyses by Aoki et al. (2012) and the comprehensive review by Shenton et al. (2012).

Individual findings for each patient: filter subject_tract_findings.csv by subject_id

Failed tract details: filter subject_failed_tracts.csv by subject_id

The Evidence That This Is Real Injury, Not Statistical Noise

1. FA decreases with severity, across every tract

Fractional Anisotropy - the primary measure of white matter health - shows a consistent, stepwise decline from normal to severe across all 13 major tracts evaluated:

Tract	Normal	Mild	Moderate	Severe
Corpus callosum forceps major	0.616	0.613	0.597	0.575
Corpus callosum body	0.538	0.534	0.513	0.496
Corpus callosum forceps minor	0.531	0.529	0.507	0.491
Inferior longitudinal fasciculus R	0.482	0.478	0.456	0.443
Inferior longitudinal fasciculus L	0.478	0.475	0.450	0.441
Cingulum R parahippocampal	0.421	0.421	0.397	0.392
Cingulum L parahippocampal	0.419	0.425	0.415	0.400
Superior longitudinal fasciculus R2	0.418	0.411	0.394	0.389
Superior longitudinal fasciculus R3	0.410	0.404	0.382	0.383
Superior longitudinal fasciculus L2	0.401	0.395	0.377	0.372
Superior longitudinal fasciculus L3	0.400	0.393	0.376	0.375
Uncinate fasciculus R	0.387	0.390	0.375	0.373
Uncinate fasciculus L	0.382	0.386	0.367	0.365

There are no exceptions. Every tract follows the same direction. This pattern cannot be produced by random chance or statistical artifact.

2. The effect sizes are large

Comparing normal to severe patients, the differences in FA are statistically large:

Tract	Cohen's d	Interpretation
Inferior longitudinal fasciculus R	0.94	Large effect
Inferior longitudinal fasciculus L	0.87	Large effect
Corpus callosum forceps major	0.78	Medium-large
Corpus callosum body	0.77	Medium-large
Superior longitudinal fasciculus L2	0.78	Medium-large
Corpus callosum forceps minor	0.72	Medium-large

Cohen's d above 0.8 is considered a large effect in clinical research. Multiple tracts exceed this threshold.

3. The pattern holds within each scanner site

To rule out the possibility that these findings are driven by differences between MRI machines, we verified the FA difference between normal and severe patients within each scanner site independently:

Scanner site	Normal mean FA	Severe mean FA	Cohen's d
Palmdale/Lancaster	0.430	0.375	1.84
Valencia	0.424	0.401	1.07
Westlake/Calabasas	0.482	0.460	0.62

At every site, normal patients have higher FA than severe patients. The effect is largest at Palmdale/Lancaster (d = 1.84, a very large effect).

4. The affected tracts match published TBI literature

The tracts most commonly damaged in the severe group are not random - they are the exact same tracts identified as vulnerable to traumatic brain injury across decades of research:

Corpus callosum - The single most consistently reported structure in TBI imaging studies. Reported in 19 of 25 studies reviewed by Hulkower et al. (2013). Vulnerable because it spans the midline and is subject to shearing forces during acceleration-deceleration injury.
Uncinate fasciculus - Reported in 8 of 25 studies (Hulkower 2013). Connects the frontal and temporal lobes; damage is associated with impaired emotional regulation and memory retrieval (Niogi et al. 2008).
Fornix - The primary output pathway of the hippocampus. FA in the fornix predicts memory outcome after TBI (Kinnunen et al. 2011).
Cingulum - Connects frontal and limbic structures. FA reductions in the cingulum are associated with impaired memory in acute mTBI (Wu et al. 2010; Mayer et al. 2010).
Superior and inferior longitudinal fasciculi - Reported in 7/25 and 5/25 studies respectively (Hulkower 2013). Support attention, language, and visual processing.

5. These findings are independent of symptom questionnaires

Symptom scores (RPQ, CHEMS) show essentially zero correlation with white matter abnormality (r = 0.03 for RPQ, r = 0.10 for CHEMS). This means the imaging findings provide independent, objective evidence that does not depend on patient self-report - a critical distinction in clinical and legal contexts where subjective symptoms may be questioned.

How to Look Up a Specific Patient

Step 1: Open subject_wm_health.csv and find the patient by subject_id. This shows their category, number of abnormal findings, and overall injury z-score.

Step 2: Open subject_tract_findings.csv and filter by the same subject_id. Each row is one abnormal finding, showing:

Which tract is affected and what it does in the brain
Which metric is abnormal (FA decreased, MD increased, or RD increased)
How many standard deviations from normal (the injury_z column)
The statistical confidence level (p < 0.05 or p < 0.01)
Published references supporting that this tract is relevant to brain injury

Step 3: Open subject_failed_tracts.csv and filter by subject_id. These are tracts that could not be reconstructed at all for this patient, even though they are successfully reconstructed in the majority of patients scanned on the same machine. This can indicate severe structural disruption of the tract.

Example: Patient sub-296

A 15-year-old male scanned at the Valencia center.

Category: Severe abnormality
70 abnormal findings (55 at p < 0.01) across 26 distinct tracts
3 tracts failed to reconstruct that succeed in >76% of patients at this site

Key findings:

Right inferior longitudinal fasciculus: radial diffusivity 5.6 SD above normal - consistent with demyelination (Song et al. 2002)
Bilateral uncinate fasciculus: FA reduced by 4.0+ SD - circuits critical for emotional regulation and episodic memory (Niogi et al. 2008)
All three corpus callosum subregions show concurrent FA reduction and MD/RD elevation - the hallmark of diffuse axonal injury (Hulkower et al. 2013)
Left fornix failed to reconstruct (91% success rate at this site) - hippocampal memory circuit (Kinnunen et al. 2011)

Output Files

File	What it contains	How to use it
subject_wm_health.csv	One row per patient with category and summary scores	Look up any patient's overall white matter health
subject_tract_findings.csv	Every abnormal finding with z-scores, tract function, and references	Build individual patient reports
subject_failed_tracts.csv	Tracts that failed to reconstruct despite high site success rates	Evidence of severe tract disruption
all_subjects_tract_stats.csv	Raw measurements for all 363 patients, all 30 tracts	Source data for independent verification

References

Hulkower MB, et al. A decade of DTI in traumatic brain injury: 10 years and 100 articles later. AJNR Am J Neuroradiol. 2013;34(11):2064-2074.
Shenton ME, et al. A review of magnetic resonance imaging and diffusion tensor imaging findings in mild traumatic brain injury. Brain Imaging Behav. 2012;6(2):137-192.
Aoki Y, et al. Diffusion tensor imaging studies of mild traumatic brain injury: a meta-analysis. J Neurol Neurosurg Psychiatry. 2012;83(9):870-876.
Song SK, et al. Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. NeuroImage. 2002;17(3):1429-1436.
Song SK, et al. Demyelination increases radial diffusivity in corpus callosum of mouse brain. NeuroImage. 2005;26(1):132-140.
Niogi SN, et al. Extent of microstructural white matter injury in postconcussive syndrome correlates with impaired cognitive reaction time. AJNR Am J Neuroradiol. 2008;29(5):967-973.
Mayer AR, et al. A prospective diffusion tensor imaging study in mild traumatic brain injury. Neurology. 2010;74(8):643-650.
Wu TC, et al. Evaluating the relationship between memory functioning and cingulum bundles in acute mild traumatic brain injury using diffusion tensor imaging. J Neurotrauma. 2010;27(2):303-307.
Kinnunen KM, et al. White matter damage and cognitive impairment after traumatic brain injury. Brain. 2011;134(Pt 2):449-463.
Fortin JP, et al. Harmonization of multi-site diffusion tensor imaging data. NeuroImage. 2017;161:149-170.
Yeh FC. DSI Studio: an integrated tractography platform and fiber data hub for accelerating brain research. Nature Methods. 2025;22(8):1617-1619. doi:10.1038/s41592-025-02762-8.
Alexander AL, et al. Diffusion tensor imaging of the brain. Neurotherapeutics. 2007;4(3):316-329.
Wilde EA, et al. Diffusion tensor imaging of acute mild traumatic brain injury in adolescents. Neurology. 2008;70(12):948-955.
Niogi SN, Mukherjee P. Diffusion tensor imaging of mild traumatic brain injury. J Head Trauma Rehabil. 2010;25(4):241-255.