Tau Imaging in Late Traumatic Brain Injury: A [ 18 F]MK-6240 Positron Emission Tomography Study

Abstract

Epidemiological studies have identified prior traumatic brain injury (TBI) as a risk factor for developing Alzheimer's disease (AD). Neurofibrillary tangles (NFTs) are common to AD and chronic traumatic encephalopathy following repetitive mild TBI. However, it is unclear if a single TBI is sufficient to cause accumulation of NFTs. We performed a [¹⁸F]MK-6240 positron emission tomography (PET) imaging study to assess NFTs in patients who had sustained a single TBI at least 2 years prior to study inclusion. Fourteen TBI patients (49 ± 20 years; 5 M/9 F; 8 moderate-severe, 1 mild-probable, 5 symptomatic-possible TBI) and 40 demographically similar controls (57 ± 19 years; 19 M/21 F) underwent simultaneous [¹⁸F]MK-6240 PET and magnetic resonance imaging (MRI) as well as neuropsychological assessment including the Cambridge Neuropsychological Test Automated Battery (CANTAB). A region-based voxelwise partial volume correction was applied, using parcels obtained by FreeSurfer v6.0, and standardized uptake value ratios (SUVR) were calculated relative to the cerebellar gray matter. Group differences were assessed on both a voxel- and a volume-of-interest-based level and correlations of [¹⁸F]MK-6240 SUVR with time since injury as well as with clinical outcomes were calculated. Visual assessment of TBI images did not show global or focal increases in tracer uptake in any subject. On a group level, [¹⁸F]MK-6240 SUVR was not significantly different in patients versus controls or between subgroups of moderate-severe TBI versus less severe TBI. Within the TBI group, One Touch Stockings problem solving and spatial working memory (executive function), reaction time (attention), and Mini-Mental State Examination (MMSE) (global cognition) were associated with [¹⁸F]MK-6240 SUVR. We found no group-based increase of [¹⁸F]MK-6240 brain uptake in patients scanned at least 2 years after a single TBI compared with healthy volunteers, which suggests that no NFTs are building up in the first years after a single TBI. Nonetheless, correlations with cognitive outcomes were found that warrant further investigation.

Introduction

Several epidemiological studies have identified prior traumatic brain injury (TBI) as an important risk factor for the development of dementia and Alzheimer's disease (AD).^1

–4 Whereas earlier research linking TBI to AD was mostly based on clinical evaluations of AD, today AD is biologically defined by means of biomarkers for the presence of amyloid plaques, tau neurofibrillary tangles (NFTs), and neurodegeneration (amyloid/tau/neurodegeneration [ATN] framework).⁵ Post-mortem studies have shown that repetitive (mild) TBI can lead to the widespread accumulation of NFTs and the development of chronic traumatic encephalopathy (CTE).⁶ The NFTs found in CTE after repetitive TBI are predominantly located in the depths of cerebral sulci, around small cerebral blood vessels,^{7

–16} whereas in AD, an NFT-specific hierarchical progression pattern has been demonstrated by Braak and Braak.¹⁷

As for pathological changes after a single TBI, conflicting results have been reported. Johnson and coworkers reported NFTs in post-mortem brains from long-term survivors of a single TBI, although significance compared with healthy controls (HC) was only reached for the subset of subjects <60 years of age.¹⁸ Using tau positron emission tomography (PET), some studies have shown increased tracer uptake after a single TBI compared with HC,^19,20 albeit by using first-generation tau PET tracers that also exhibit off-target binding to monoamine oxidase (MAO)-B and α-synuclein.²¹ Different studies using the same tau PET tracer reported conflicting outcomes with some demonstrating increased [¹⁸F]Flortaucipir uptake after a single TBI,^22,23 whereas others failed to do so.²⁴ Hicks and coworkers recently investigated [¹⁸F]MK-6240 uptake in a large sample of patients who sustained a single moderate-to-severe TBI at least 10 years prior to study inclusion and found no significantly higher tracer uptake in comparison with HC.²⁵ Taken together, all available evidence suggests that NFTs can develop after a single TBI, although 10 years after the event, NFT-binding tracers such as [¹⁸F]MK-6240 do not seem to show group differences compared with HC. It is unclear if at 10 years post- injury, NFTs are either not yet formed or may have already been cleared from the brain through tau clearance mechanisms such as proteasomal degradation and autophagy.^26,27

This study aims to investigate the presence of NFTs using [¹⁸F]MK-6240 PET-magnetic resonance (MR) in subjects at least 2 years after a single TBI as compared with HC. Moreover, we explored the correlation of [¹⁸F]MK-6240 binding with time since injury and with cognitive outcome as assessed by a neuropsychological test battery.

Methods

Participants

Eligible patients were at least 18 years old and had sustained a single TBI at least 2 years prior to study inclusion. Patients were referred by either neurosurgical or psychiatric centers. Severity was assessed according to the Mayo Classification.²⁸ Main exclusion criteria were history of neurodegenerative disorders prior to the head trauma and contraindication for magnetic resonance imaging (MRI). HC between 18 and 85 years old were recruited through local newspapers and Internet advertisements. Physical and mental health were thoroughly assessed. Exclusion criteria for HC were history of major neurological, psychiatric, or internal pathology; history of alcohol or other drug abuse; and MR abnormalities including brain white matter disease Fazekas 3. All subjects underwent cognitive screening consisting of a neuropsychological test battery with following subtests of the Cambridge Neuropsychological Test Automated Battery (CANTAB)²⁹: Reaction Time (RTI), Paired Associates Learning (PAL), Spatial Working Memory (SWM), Multitasking Test (MTT), and One Touch Stockings of Cambridge (OTS). A detailed description of these tests can be found at www.cantab.com,²⁹ and the corresponding obtained parameters are explained in Table S1. Global cognition was evaluated by the Mini-Mental State Examination (MMSE), and depressive symptoms were assessed using the Beck Depression Inventory (BDI) and the 90-item Symptom CheckList (SCL-90).

The study was approved by the local ethics committee and was conducted according to the latest version of the Declaration of Helsinki. Written informed consent was signed by all participants prior to study inclusion. This study is part of a larger, multimodal, multi-tracer imaging study that also included other patient populations (ClinicalTrials.gov: NCT03514524).

Data acquisition

[¹⁸F]MK-6240 was synthesized as previously described.³⁰ All subjects underwent a 30-min PET-MR scan, acquired 90–120 min post-injection of 100-150 MBq [¹⁸F]MK-6240. All scans were performed on an integrated time-of-flight 3T PET-MR scanner (Signa, GE Healthcare, Milwaukee, WI, USA). [¹⁸F]MK-6240 PET acquisitions were re-binned into six frames of 5 min and reconstructed using an ordered subset expectation maximization (OSEM) algorithm (4 iterations and 28 subsets), with corrections for scatter, random coincidences, dead time, and radioactive decay. Attenuation correction was zero-echo time (ZTE) MR-based as reported previously.³¹ Three-dimensional isotropic 4 mm Gaussian smoothing was applied to reduce image noise.

Simultaneously with the PET scan, three-dimensional (3D) T1-weighted MR images [(plane: sagittal; echo time [TE]: 3.2 ms; repetition time [TR]: 8.5 ms; inversion time [TI]: 450 ms; flip angle: 12 degrees; receiver bandwidth: 31.25 kHz; number of excitations [NEX]: 1; voxel size: 1 x 1 x 1 mm) as well as 3D T2-weighted fluid-attenuated inversion recovery (FLAIR) images (plane: sagittal; TE: 137 ms; TR: 8500 ms; TI: 2298 ms; receiver bandwidth: 31.25 kHz; NEX: 1; voxel size: 0.7 x 1 x 1 mm) were acquired using a vendor-supplied 8-channel or 32-channel brain phased array head coil.

Subjects >50 years of age also underwent an amyloid [¹⁸F]NAV4694 or [¹¹C]Pittsburgh compound B (¹¹C-PiB) PET scan on the same PET-MR scanner or on a PET-computed tomography (CT) scanner (Biograph TruePoint, Siemens, Erlangen, Germany), respectively. [¹⁸F]NAV4694 and [¹¹C]PiB scans were reconstructed using an OSEM algorithm, and 3D isotropic 4.5 mm and 2 mm Gaussian smoothing were applied, respectively. Amyloid status was visually assessed and classified as positive or negative by an expert nuclear medicine physician (K.V.L.).

PET analysis

Reconstructed [¹⁸F]MK-6240 images were corrected for motion artifacts with PMOD software (v4.1, PMOD Inc. Zurich, Switzerland) using a rigid frame-by-frame co-registration to the first frame. Subsequently, all motion-corrected frames were averaged and rigidly co-registered to the corresponding T1-weighted MRI. Also, T2-weighted FLAIR images were co-registered to the corresponding T1-weighted MRI. FreeSurfer v6.0 (Laboratory for Computational Neuroimaging v6.0, Boston, MA, USA) was used for T1- and T2 FLAIR-based MR segmentation and automated parcellation of the volumes of interest (VOIs).^32
–34 A region-based voxelwise (RBV) partial volume correction was applied to the standardized uptake value (SUV) maps,³⁵ where the PET resolution was modeled as a 3D isotropic Gaussian kernel with a full width at half maximum (FWHM) of 5 mm. Next, SUV ratios (SUVR) were calculated using the full cerebellar cortex as a reference region.³⁶ Based on regions with increased tau as obtained by other PET studies in TBI,^{18
–20,22,23} eight composite, bilateral gray matter (GM) VOIs were a priori defined and derived from the FreeSurfer parcellation: frontal, parietal, occipital, temporal, mesotemporal (entorhinal cortex, parahippocampal gyrus, hippocampus, and amygdala) (MTL), and anterior cingulate cortex, as well as the insula and thalamus.

For the voxel-based analyses, SUVR maps were spatially normalized to Montreal Neurological Institute (MNI) space using a non-linear registration as obtained by the CAT12 toolbox of Statistical Parametric Mapping (SPM12; Welcome Trust Centre for Neuroimaging, University College, London, UK) and smoothed using an isotropic Gaussian kernel with 8 mm FWHM (voxel size: 1.5 x 1.5 x 1.5 mm).

Statistical analysis

Statistical analyses were performed in GraphPad Prism version 9 (GraphPad Software, La Jolla, CA, USA). Data are presented as mean ± standard deviation (SD) unless otherwise specified. Normality of the data distributions was assessed by Shapiro–Wilk tests (α = 0.05). Demographic characteristics were compared between groups using an unpaired Student t test, unpaired Mann–Whitney U test, Fischer's exact test, χ² test, or χ² test for trend as appropriate.

Using spatially normalized and smoothed images as described, voxel-based z-score maps were generated for each patient versus the HC group. Additionally, for each HC, a voxel-based z-score map was calculated versus the remainder of the HC group. A z-score of >2.5 was considered positive.

For the VOI-based analysis, mean regional [¹⁸F]MK-6240 SUVRs were compared between TBI and HC using an unpaired Student t test or Mann–Whitney U test as appropriate at a significance level of α = 0.006 (Bonferroni corrected for 8 VOIs).

The voxel-based group comparison was performed using an unpaired t test in SPM with thresholds for cluster height, voxel height, and cluster extent of p < 0.05 (familywise error [FWE] corrected), p < 0.001 (uncorrected), and k_ext > 200 voxels, respectively. To exclude extracerebral clusters, a binary GM mask was applied. Correlations between [¹⁸F]MK-6240 SUVR and time since injury were assessed within the TBI group and correlations between [¹⁸F]MK-6240 SUVR and neuropsychological test scores were assessed within the combined cohort of TBI patients and HC, both by Pearson or Spearman correlation coefficients as appropriate at a significance level of α = 0.05. As this was an exploratory analysis, no correction for multiple comparisons was applied for this correlation analysis.

Results

Participants and neuropsychological evaluations

Fourteen patients with moderate-to-severe (n = 8), mild probable (n = 1), or symptomatic possible (n = 5) TBI were included and received a [¹⁸F]MK-6240 PET-MR scan on average 5.6 ± 2.9 years (range 2.0–10.8) post injury. Patient characteristics are summarized in Table 1.

Table 1.

Patient Characteristics

Patient	Age at PET-MR scan	Sex	Time since injury (years)	Mechanism of injury	Amyloid PET status	MRI (post-traumatic abnormalities on MRI simultaneously obtained with PET)	Mayo classification
A	26	M	5.2	TA	n/a	Multiple microbleeds (DAI); larger hemorrhagic remnant in the right frontal lobe	Mod/sev TBI
B	31	F	7.2	TA	n/a	Contusional scar with surrounding gliosis in the right temporal lobe	Mod/sev TBI
C	61	F	8.2	Fall	Pos	Subarachnoid hemorrhagic remnants right frontal lobe	Mild/prob TBI
D	59	M	10.8	TA	Neg	Normal	Sympt/poss TBI
E	48	F	6.5	TA	n/a	Normal	Sympt/poss TBI
F	33	F	3.3	TA	n/a	Normal	Sympt/poss TBI
G	46	F	7.3	TA	n/a	Normal	Sympt/poss TBI
H	23	M	5.0	TA	n/a	Normal	Sympt/poss TBI
I	26	M	8.3	Fall	n/a	Contusional scar with surrounding gliosis in the left frontal lobe	Mod/sev TBI
J	36	F	8.7	TA	n/a	Microbleeds in the splenium corpus callosi (DAI); right frontal hemorrhagic remnants from subdural hematoma	Mod/sev TBI
K	75	F	2.0	TA	Neg	Contusional remnants with surrounding gliosis right frontal and temporal lobes	Mod/sev TBI
L	82	M	2.0	TA	Pos	Hemorrhagic remnants in the frontal lobes, bilateral hygroma with hemorrhagic components	Mod/sev TBI
M	66	F	2.3	Fall	Neg	Normal	Mod/sev TBI
N	70	F	2.2	Fall	Neg	Contusional scar with surrounding gliosis in the left temporal lobe; microbleeds (DAI)	Mod/sev TBI

Patient labels as shown in Figure 1.

PET, positron emission tomography; MRI, magnetic resonance imaging; DAI, diffuse axonal injury; Mild/prob TBI = mild probable traumatic brain injury; Mod/sev TBI = moderate/severe TBI; n/a = not available; Neg = negative; Pos = positive; Sympt/poss TBI = symptomatic possible TBI; TA = traffic accident.

For comparison, a demographically similar group of 40 HC was included. Injected activity was 121 ± 15 MBq (specific activity of 584 ± 250 GBq/μmol) and 138 ± 26 MBq (specific activity of 593 ± 464 GBq/μmol) for TBI patients and HC, respectively. Group characteristics are shown in Table 2.

Table 2.

Demographic Information on Healthy Control and TBI Groups

	Healthy controls	TBI patients	p value
Number of subjects, n	40	14
Age, y	57 ± 19	49 ± 20	0.2
Sex, M/F	19/21	5/9	0.5
Education, n			0.4
Secondary education	10	7
Higher education	20	3
University	10	4
CANTAB
RTI median five-choice movement time	270 ± 83	356 ± 121	0.1
PAL first attempt memory score	11.3 ± 4.0	9.71 ± 6.0	0.3
SWM strategy	7.20 ± 2.7	8.07 ± 2.9	0.2
MTT multitasking cost	326 ± 143	88.3 ± 1041	0.8
OTS problems solved on first choice	15.8 ± 3.5	15.0 ± 4.7	0.8
MMSE, 0-30	29.6 ± 0.7	27.6 ± 3.7	0.0007
BDI, 0-63	3.16 ± 2.9	13.0 ± 11	0.003
SCL-90, 90-450	103 ± 11	162 ± 58	< 0.0001
Amyloid status, n			0.2
Positive	4	2
Negative	22	4
N/a (< 50 y)	14	8

Data are presented as mean ± standard deviation (SD) when appropriate.

Significant findings are indicated in bold.

BDI, Beck Depression Inventory; CANTAB, Cambridge Neuropsychological Test Automated Battery; MMSE, Mini-Mental State Examination; MTT, Multitasking Test; OTS, One Touch Stockings of Cambridge; PAL, Paired Associates Learning; RTI, Reaction Time; SCL-90, 90-item Symptoms Checklist; SWM, Spatial Working Memory; TBI, traumatic brain injury.

Six TBI patients and 26 HC received amyloid PET scans of which two and four were A positive, respectively. Groups did not significantly differ concerning age, sex, or education. TBI patients showed significantly lower scores on MMSE and had significantly higher BDI and SCL-90 scores. No significant differences in CANTAB domains were observed.

[¹⁸F]MK-6240 in individual patients with TBI

[¹⁸F]MK-6240 images are shown for all TBI patients in Figure 1. Visually, none of these patients showed a clear focal increase in tracer uptake in GM areas. Two patients (panels D and L) showed slightly increased global tracer uptake. Amyloid-positive TBI patients were those in panels C and L. For comparison, panel O shows an [¹⁸F]MK-6240 SUVR image of a healthy control. As for a semiquantitative voxel-based analysis, all SUVR images were z-score transformed and a z-score of 2.5 was considered positive. As a result, in the TBI group a median of 2098 voxels (interquartile range [IQR]: 242–10524) were positive at this threshold whereas in the HC group, a median of 1024 voxels (IQR: 279–3407) were positive (p = 0.53).

FIG. 1.

[¹⁸F]MK-6240 standardized uptake value ratios (SUVR) images from traumatic brain injury (TBI) patients and a healthy control. This figure shows lack of increased tracer uptake in TBI patients. Corresponding information is shown in Table 1. Amyloid positive TBI patients are shown in panels C and L. A healthy volunteer is shown in panel O. Images are shown in radiological convention.

[¹⁸F]MK-6240 SUVR in TBI versus HC

Results of the VOI-based group comparison are shown in Figure 2. [¹⁸F]MK-6240 SUVR was not significantly different between TBI patients and HC in any of the assessed VOIs (Table S2), nor between subgroups of moderate-severe TBI versus less severe TBI (Table S3). Voxel-based analysis indicated a cluster with higher [¹⁸F]MK-6240 SUVR in the left cerebellum (according to automated anatomical labeling [AAL] atlas) in TBI patients than in HC (cluster p_FWE = 0.049, k_ext = 1261 voxels).

FIG. 2.

[¹⁸F]MK-6240 standardized uptake value ratios (SUVR) volume of interest (VOI)-based group comparison. This figure shows the results of the [¹⁸F]MK-6240 SUVR VOI-based group comparison between healthy controls (HC) (shown in blue) and traumatic brain injury (TBI) patients (shown in red). Amyloid status of TBI patients is indicated as unknown (square), negative (open triangle), or positive (full triangle). HC subject with high mesotemporal [¹⁸F]MK-6240 SUVR was amyloid negative.

Association with time since injury and clinical outcome

Time since injury was not significantly associated with [¹⁸F]MK-6240 SUVR in the TBI group (Table S4).

Additionally, in the combined cohort of TBI patients and HC, RTI median five-choice movement time, SWM strategy, MMSE, BDI, and SCL-90 were not significantly associated with [¹⁸F]MK-6240 SUVR in any of the assessed VOIs. On the other hand, OTS problems solved on first choice was significantly associated with [¹⁸F]MK-6240 SUVR in the anterior cingulate VOI (r_s = -0.38 ; p = 0.004; Fig. 3), mesotemporal VOI (r_s = -0.38; p = 0.005), and thalamus VOI (r_s = -0.34; p = 0.01) but not in any of the other assessed VOIs. PAL first attempt memory score was significantly associated with [¹⁸F]MK-6240 SUVR in the anterior cingulate (r_s = -0.30; p = 0.03; Fig. 3) but not in any of the other assessed VOIs. MTT multitasking cost was significantly associated with [¹⁸F]MK-6240 SUVR in the frontal VOI (r_s = -0.36; p = 0.008) (Table S5). Of note, upon outlier exclusion MTT multitasking cost remained significantly associated with [¹⁸F]MK-6240 SUVR in the frontal VOI (r_s = -0.35; p = 0.009). These correlations were also assessed within the TBI group separately. Here, RTI median five-choice movement time was significantly associated with [¹⁸F]MK-6240 SUVR in the mesotemporal VOI (r_s = 0.64; p = 0.02) and anterior cingulate (r_p = 0.57; p = 0.03). SWM strategy was significantly associated with [¹⁸F]MK-6240 SUVR in the mesotemporal VOI (r_s = 0.58; p = 0.03). OTS problems solved on first choice was significantly associated with [¹⁸F]MK-6240 SUVR in the anterior cingulate VOI (r_p = -0.63; p = 0.02), and MMSE was significantly associated with [¹⁸F]MK-6240 SUVR in the insula VOI (r_s = -0.61; p = 0.02) (Table S6).

FIG. 3.

[¹⁸F]MK-6240 standardized uptake value ratios (SUVR) volume of interest (VOI)-based correlations with neuropsychological test scores. This figure shows the significant correlations of [¹⁸F]MK-6240 SUVR with neuropsychological test scores One Touch Stockings of Cambridge (OTS) problems solved on first choice and Paired Associates Learning (PAL) first attempt memory score. Correlation coefficients and p values denote correlation analysis within the combined cohort of traumatic brain injury (TBI) patients and healthy controls. Healthy controls are indicated in blue and TBI patients are indicated in red. Amyloid status of TBI patients is indicated as unknown (square), negative (open triangle), or positive (full triangle).

Discussion

In this study, we investigated NFT deposition at least 2 years after a single TBI using [¹⁸F]MK-6240 PET. Our data showed no group differences in tracer uptake between TBI and HC, neither in terms of individual z-scores deviation volume, nor in a direct VOI-based or voxel-based group comparison. Our results are in agreement with a recent Australian study by Hicks and coworkers indicating no increased [¹⁸F]MK-6240 binding at least 10 years after a single TBI.²⁵ Whereas Weiner and coworkers investigated tau changes in a mixed group of single and multiple TBI and found no significantly higher tau [¹⁸F]Flortaucipir PET deposition compared with HC,²⁴ another study by Clark and coworkers also examined a mixed single/multiple TBI group and found an increase in cerebrospinal fluid (CSF) phospho-tau (p-tau) and total-tau (t-tau).³⁷ It has been shown in AD patients that CSF p-tau starts deviating from normal values before tau PET uptake increases,³⁸ which might also explain the discrepant findings between PET imaging and CSF measures in these mixed TBI cohorts. Interestingly, Wooten and coworkers assessed [¹⁸F]Flortaucipir uptake across the frequency spectrum of TBI and found tracer uptake to be the highest in a patient who had sustained a single severe TBI 2.5 years prior to PET, whereas tracer uptake in most former contact sports athletes who sustained multiple TBIs was comparable to that in controls.³⁹ Therefore, severity of TBI might also be an important variable next to TBI frequency in subsequent tau accumulation after TBI.

The lack of increased [¹⁸F]MK-6240 uptake after TBI in our study sample could be explained by several hypotheses. First, it could be caused by the true absence of NFTs where either no NFTs accumulated after a single TBI, NFTs were not formed yet, or the formed NFTs were already cleared. Little is known about the rate at which NFTs are formed. However, it has been shown that local replication, as opposed to spreading across brain regions, is the predominant process controlling the global rate of tau accumulation in AD.⁴⁰ As such, it might be possible that an NFT conformation is not (yet) formed but that toxic, soluble tau oligomers are present. However, a contribution of such tau oligomers cannot be investigated using [¹⁸F]MK-6240 PET, because this tracer binds selectively to NFTs.⁴¹ More specifically, [¹⁸F]MK-6240 binds to 3R/4R NFTs as found not only in AD but also in CTE.¹⁵ Therefore, if NFTs were formed after TBI in high concentration, they should have been visualized by the tracer. In addition, it is still unclear which factors such as TBI severity or location of the impact are crucial for the formation of NFTs after TBI. Concerning the hypothesis that NFTs were already removed, it has been demonstrated that clearance of tau oligomers and NFTs is mediated by proteasomal degradation and autophagy.^26,27 It is known that these clearance mechanisms are impaired in AD, but their functioning in CTE is yet to be elucidated and the rate at which tau clearance takes place is still unclear. Because a lack of increased tracer uptake at least 10 years after a single TBI was also shown by Hicks and coworkers,²⁵ it is unlikely that NFTs will still be formed during the years after the 2–10 year range that we examined here. Although this seems unlikely based on previous literature findings and suggested mechanisms, we cannot fully rule out the possibility that NFTs were formed early and in small quantities, but may have been cleared within the first 2 years after the injury.

Second, tracer uptake was quantified by means of SUVR. Therefore, perfusion changes are not considered in the quantification. We used the cerebellar cortex as a reference region as was previously validated in HC and AD,³⁶ and did not find a significant difference in partial volume corrected SUV in the cerebellar cortex between both groups (data not shown). Nonetheless, the validation of this reference region in TBI was not yet performed. It is of note that neuronal loss and sparse NFTs have been reported in the cerebellum in relation to CTE.^4,7 Although NFTs in the cerebellum in CTE are unlikely to confound PET analysis, to exclude the possibility that our findings were driven by our choice of reference region, we also quantified the images using pons as a reference region.⁴² Results did not change and confirmed that no significant differences in tracer uptake could be found between the TBI and HC groups in any of the VOIs (data not shown).

Experimental animal studies have shown toxic tau oligomers shortly after a single TBI,⁴³ but to the best of our knowledge, tau imaging shortly after TBI has yet to be performed in humans. Importantly, disruption of the blood–brain barrier (BBB) can occur as a result of TBI,⁴ which implicates that polar tracer metabolites can interfere with the PET signal,⁴⁴ making interpretation of such results challenging.

Within the combined cohort of TBI patients and HC, the scores of neuropsychological tests covering domains of executive functioning (OTS, MTT) and memory (PAL) were significantly associated with [¹⁸F]MK-6240 SUVR. However, none of these associations remained significant after correction for multiple comparisons. Whereas many studies have shown significant correlations of the tau PET signal with cognitive outcomes in mild cognitive impairment or AD,^45

–48 these correlations were consistently lacking when examining TBI cohorts.^19,23 Additionally, CSF p-tau and t-tau were not associated with measures for executive functioning, verbal learning, or memory in a mixed single-multiple TBI cohort.³⁷ Moreover, the correlation we found between [¹⁸F]MK-6240 SUVR and MTT multitasking cost (r_s = -0.36; p = 0.008) is negative, whereas a positive correlation was expected because an increase in MTT multitasking cost scores indicates worse multitasking and was assumed to be correlated with increased [¹⁸F]MK-6240 SUVR. This highlights the need for further investigation of the correlations of [¹⁸F]MK-6240 SUVR with neuropsychological test scores in larger TBI samples.

This study comes with a number of limitations. The sample of TBI patients was quite small and heterogeneous, which might have resulted in insufficient power to demonstrate differences on the group level. However, our z-score analysis together with a thorough visual inspection of the images supported our group-level findings that are concordant with the results obtained by Hicks and coworkers.²⁵ Because of the small subset of patients who received an amyloid PET scan, the influence of amyloid plaques on tau deposition as measured by [¹⁸F]MK-6240 could not be evaluated and requires further investigation. However, both patients with positive amyloid PET were >60 years of age, so the chance of amyloid positivity as part of the normally increased incidence with ageing is possible.⁴⁹ In addition, genotyping for apolipoprotein E (ApoE), of which the presence of the ApoE ɛ4 allele increases the risk of developing sporadic AD,⁵⁰ was not performed. It has been hypothesized that for subjects who are genetically predisposed to develop AD pathology, sustaining a TBI could increase susceptibility to developing NFTs,^13,51 although this was contradicted by others.^7,52 Last, as mentioned, tracer quantification was performed using SUVR, which does not account for tracer influx and washout and can therefore be influenced by perfusion changes. Nonetheless, it was previously demonstrated that late-time point [¹⁸F]MK-6240 SUVR is closely correlated to full dynamic tracer quantification,³⁶ and that perfusion changes of ±25% only slightly influence [¹⁸F]MK-6240 SUVR quantification.⁵³ Moreover, as patients were scanned at least 2 years after their TBI, changes in BBB permeability at this time point are unlikely.

Conclusion

In conclusion, we found no group-based increase of [¹⁸F]MK-6240 brain uptake in patients scanned at least 2 years after a single TBI compared with healthy volunteers, which suggests that no NFTs are building up in the first years after a single TBI. However, significant correlations with cognition scores were found that warrant further investigation in a larger sample.

Transparency, Rigor, and Reproducibility Summary

The study was pre-registered at clinicaltrials.gov (NCT03514524). The analysis plan was not formally pre-registered, but the team member with primary responsibility for the analysis (G. Vanderlinden) certifies that the analysis plan was pre-specified. A sample size of 14 subjects per group was planned based on an expected group difference in tracer uptake of 0.5 SUVR with SD of 0.5, calculated to yield 80% power to detect a significant group difference using a two sample t test with a p value <0.05. A total of 19 potential participants were screened, and imaging data were obtained and successfully analyzed in 14. Human participants were blinded to results of the imaging assessments throughout the study, even after clinical assessments were complete. Imaging data were acquired between December 17, 2020 and November 24, 2022. Imaging data were collected with subjects in the fed state on the same GE Signa 3T PET-MR scanner. All imaging data sets were analyzed at the same time. The time required for image acquisition was 30 min. The spatial resolution was 5 mm. Complete imaging parameters are provided in the Methods section. All equipment and software used to perform acquisition are widely available (Signa, GE Healthcare, Milwaukee, WI, USA; GraphPad Software, La Jolla, CA, USA). The key inclusion criteria (e.g., primary diagnosis or prognostic factor) are established standards in the field. The statistical tests used were based on the assumption of normal distributions that were tested using a Shapiro–Wilk test. If data were not normally distributed, the appropriate non-parametric alternative was applied. Bonferroni and FWE correction for multiple comparisons were performed. To our knowledge, no replication or external validation studies have been performed or are planned/ongoing at this time. Anonymized data will be deposited in an access-controlled file server used by the academic research PET imaging group, which can be shared upon reasonable request from any qualified investigator on approval by the ethics committee of the local university hospital. Analytical code used to conduct the analyses presented in this study are not available in a public repository. It may be available by e-mailing the corresponding author. The authors agree to provide the full content of the manuscript on request made by contacting the corresponding author.

Footnotes

Acknowledgments

We thank all the participants for their willingness to participate in this study. We are grateful to the PET-MR technologists, in particular Kwinten Porters and Jef Van Loock for their contribution in data acquisition. We also thank the PET radiopharmacy team and nuclear medicine medical physics team for their skilled contributions.

Authors' Contributions

K.V.L., M.V., R.L., G.V., and L.M. contributed to study concept and design. K.V.L. and M.V. supervised the study progression. G.V., L.M., B.D., J.V.W., and D.L. recruited study participants. G.V. and L.M. contributed to data acquisition. G.V., L.M., M.K., M.V., and K.V.L. contributed to data analysis and interpretation. K.V.L., R.L., and M.V. obtained funding. G.V. and K.V.L. drafted the manuscript. Manuscript revision was performed by all authors.

Funding Information

This study was supported by a Fonds Wetenschappelijk Onderzoek (FWO) grant (FWO/G093218N) and by KU Leuven internal C2 funding (C24-17-063).

Author Disclosure Statement

K.V.L. and R.L. performed this study as senior investigators of FWO Flanders. K.V.L. is an advisory board member of Cerveau and has received fees through KU Leuven for consultancy activities for GE Healthcare. K.V.L. and M.K. have performed contract research through KU Leuven for Merck, Janssen Pharmaceuticals, UCB, Cerveau, Syndesi, Eikonizo, GE Healthcare, Cerevel, BMS, and Curasen. No other potential conflicts of interest relevant to this article exist.

Supplementary Material

Supplementary Table S1

Supplementary Table S2

Supplementary Table S3

Supplementary Table S4

Supplementary Table S5

Supplementary Table S6

References

Nordström

, Nordström

Traumatic brain injury and the risk of dementia diagnosis: A nationwide cohort study. PLoS Med, 2018; 15(1):e1002496; doi: 10.1371/JOURNAL.PMED.1002496

, Li

, et al. Head Injury as a risk factor for dementia and Alzheimer's disease: a systematic review and meta-analysis of 32 observational studies. PLoS One, 2017; 12(1):e0169650; doi: 10.1371/JOURNAL.PONE.0169650

Guo

, Cupples

A., Kurz A., et al. Head injury and the risk of AD in the MIRAGE study. Neurology, 2000; 54(6):1316–1323; doi: 10.1212/wnl.54.6.1316

Mckee

C., Daneshvar D. H. The neuropathology of traumatic brain injury. Handb Clin Neurol, 2015; 127:45–66; doi: 10.1016/B978-0-444-52892-6.00004-0

Jack Jr

C. R.

, Bennett

D. A.

, Blennow

, et al. NIA-AA research framework: toward a biological definition of Alzheimer's disease. Alzheimers Dement, 2018; 14:535–562; doi: 10.1016/j.jalz.2018.02.018

Nowinski

J., Bureau S. C., Buckland M. E., et al. Applying the Bradford Hill Criteria for causation to repetitive head impacts and chronic traumatic encephalopathy. Front Neurol, 2022; 13:938163; doi: 10.3389/fneur.2022.938163

McKee

C., Stein T. D., Nowinski C. J., et al. The spectrum of disease in chronic traumatic encephalopathy. Brain, 2013; 136:43–64; doi: 10.1093/BRAIN/AWS307

Mckee

C., Cairns N. J., Dickson D. W., et al. The first NINDS/NIBIB consensus meeting to define neuropathological criteria for the diagnosis of chronic traumatic encephalopathy. Acta Neuropathol, 2016; 131:75–86; doi: 10.1007/s00401-015-1515-z

Dickstein

L., De Gasperi R., Gama Sosa M. A., et al. Brain and blood biomarkers of tauopathy and neuronal injury in humans and rats with neurobehavioral syndromes following blast exposure. Mol Psychiatry, 2021; 26:5940–5954; doi:10.1038/s41380-020-0674-z

10.

Stern

A., Adler C. H., Chen K., et al. Tau positron-emission tomography in former National Football League players. N Engl J Med, 2019; 380:1716–25; doi: 10.1056/NEJMoa1900757

11.

Mez

, Daneshvar

H., Kiernan P. T., et al. Clinicopathological evaluation of chronic traumatic encephalopathy in players of American Football. JAMA, 2017; 318 (4):360–370; doi: 10.1001/JAMA.2017.8334

12.

Bieniek

F., Ross O. A., Cormier K. A., et al. Chronic traumatic encephalopathy pathology in a neurodegenerative disorders brain bank. Acta Neuropathol, 2015; 130:877–889; doi: 10.1007/S00401-015-1502-4

13.

McKee

C., Cantu R. C., Nowinski C. J., et al. Chronic traumatic encephalopathy in athletes: Progressive tauopathy after repetitive head injury. J Neuropathol Exp Neurol, 2009; 68(7):709–735; doi: 10.1097/NEN.0b013e3181a9d503

14.

Katsumoto

, Takeuchi

, Tanaka

Tau pathology in chronic traumatic encephalopathy and Alzheimer's disease: similarities and differences. Front Neurol, 2019; 980; doi: 10.3389/fneur.2019.00980

15.

Villemagne

L., Okamura N. Tau imaging in the study of ageing, Alzheimer's disease, and other neurodegenerative conditions. Curr Opin Neurobiol, 2016; 36:43–51; doi: 10.1016/J.CONB.2015.09.002

16.

Varlow

, Vasdev

Evaluation of tau radiotracers in chronic traumatic encephalopathy. J Nucl Med, 2023; 64(3):460–465; doi: 10.2967/jnumed.122.264404

17.

Braak

, Braak

Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol, 1991; 82(4):239–259; doi: 10.1007/BF00308809

18.

Johnson

E., Stewart W., Smith D. H. Widespread tau and amyloid-beta pathology many years after a single traumatic brain injury in humans. Brain Pathol, 2012; 22:142–149; doi: 10.1111/j.1750-3639.2011.00513.x

19.

Takahata

, Kimura

, Sahara

, et al. PET-detectable tau pathology correlates with long-term neuropsychiatric outcomes in patients with traumatic brain injury. Brain, 2019; 142:3265–3279; doi: 10.1093/brain/awz238

20.

Marklund

, Vedung

, Lubberink

, et al. Tau aggregation and increased neuroinflammation in athletes after sports-related concussions and in traumatic brain injury patients-a PET/MR study. NeuroImage Clin, 2021; 30:102665; doi:10.1016/j.nicl.2021.102665

21.

Lemoine

, Leuzy

, Chiotis

, et al. Tau positron emission tomography imaging in tauopathies: The added hurdle of off-target binding. Alzheimers Dement (Amst), 2018; 10:232–236; doi: 10.1016/j.dadm.2018.01.007

22.

Mohamed

Z., Cumming P., Götz J., et al. Tauopathy in veterans with long-term posttraumatic stress disorder and traumatic brain injury. Eur J Nucl Med Mol Imaging, 2019; 46(5):1139–1151; doi: 10.1007/s00259-018-4241-7

23.

Gorgoraptis

, Li

L. M.

, Whittington

, et al. In vivo detection of cerebral tau pathology in long-term survivors of traumatic brain injury. Sci Transl Med, 2019; 11:eaaw1993; doi: 10.1126/SCITRANSLMED.AAW1993/SUPPL_FILE/AAW1993_SM.PDF

24.

Weiner

M. W.

, Harvey

, Landau

S. M.

, et al. Traumatic brain injury and post-traumatic stress disorder are not associated with Alzheimer's disease pathology measured with biomarkers. Alzheimers Dement, 2022; 29:10.1002/alz.12712; doi: 10.1002/ALZ.12712

25.

Hicks

, Ponsford

L., Spitz G., et al. Amyloid- and tau imaging in chronic traumatic brain injury: a cross-sectional study. Neurology, 2022; 99(11):e1131-e1141; doi: 10.1212/WNL.0000000000200857

26.

Hamano

, Enomoto

, Shirafuji

, et al. Autophagy and tau protein. Int J Mol Sci, 2021; 22:7475; doi: 10.3390/IJMS22147475

27.

Chesser

A. S.

, Pritchard

S. M.

, Johnson

G. V. W.

Tau clearance mechanisms and their possible role in the pathogenesis of Alzheimer disease. Front Neurol, 2013; 4:122; doi: 10.3389/FNEUR.2013.00122

28.

Malec

J.F.

, Brown

A. W.

, Leibson

C. L.

, et al. The mayo classification system for traumatic brain injury severity. J Neurotrauma, 2007; 24(9):1417–1424; doi: 10.1089/NEU.2006.0245.

29.

Cambridge Cognition. CANTAB® Cognitive assessment software; Cambridge Cognition, Cambridge, 2019. Available from: www.cantab.com.

30.

Collier

T. L.

, Yokell

D. L.

, Livni

, et al. cGMP production of the radiopharmaceutical [ 18 F]MK-6240 for PET imaging of human neurofibrillary tangles. J Label Compd Radiopharm, 2017; 60:263–269; doi: 10.1002/jlcr.3496

31.

Schramm

, Koole

, Willekens

M. A., et al. Regional accuracy of ZTE-based attenuation correction in static [18F]FDG and dynamic [18F]PE2I brain PET/MR. Front Phys, 2019; 7:211; doi: 10.3389/fphy.2019.00211

32.

Fischl

, Salat

, Busa

, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron, 2002; 33:341–355; doi: 10.1016/s0896-6273(02)00569-x

33.

Desikan

S., Ségonne F., Fischl B., et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage, 2006; 31:968–980; doi: 10.1016/j.neuroimage.2006.01.021

34.

Fischl

, van der Kouwe

, Destrieux

, et al. Automatically parcellating the human cerebral cortex. Cortex, 2004; 14:11–22; doi: 10.1093/cercor/bhg087

35.

Mertens

, Michiels

, Vanderlinden

, et al. Impact of meningeal uptake and partial volume correction techniques on [ 18 F] MK-6240 binding in aMCI patients and healthy controls. J Cereb Blood Flow Metab, 2022; 41(11):1–11; doi: 10.1177/0271678X221076023

36.

Pascoal

T. A.

, Shin

, Kang

M. S.

, et al. In vivo quantification of neurofibrillary tangles with [ 18 F]MK-6240. Alzheimers Res Ther, 2018; 10(1):74; doi:10.1186/s13195-018-0402-y

37.

Clark

A. L.

, Weigand

A. J.

, Bangen

K. J.

, et al. Higher cerebrospinal fluid tau is associated with history of traumatic brain injury and reduced processing speed in Vietnam-era veterans: a Department of Defense Alzheimer's Disease Neuroimaging Initiative (DOD-ADNI) study. Alzheimers Dement (Amst), 2021; 13:e12239; doi: 10.1002/DAD2.12239

38.

Groot

, Smith

, Stomrud

, et al. Phospho-tau with subthreshold tau-PET predicts increased tau accumulation rates in amyloid-positive individuals. Brain, 2023; 19;146(4):1580-1591; doi: 10.1093/brain/awac329

39.

Wooten

W., Ortiz-Terán L., Zubcevik N., et al. Multi-modal signatures of tau pathology, neuronal fiber integrity, and functional connectivity in traumatic brain injury. J Neurotrauma, 2019; 36:3233–3243; doi: 10.1089/NEU.2018.6178

40.

Meisl

, Hidari

, Allinson

, et al. In vivo rate-determining steps of tau seed accumulation in Alzheimer's disease. Sci Adv, 2021; 7:eabh1448; doi: 10.1126/SCIADV.ABH1448

41.

Aguero

, Dhaynaut

, Normandin

D., et al. Autoradiography validation of novel tau PET tracer [F-18]-MK-6240 on human postmortem brain tissue. Acta Neuropathol Commun, 2019; 7(1):37; doi: 10.1186/s40478-019-0686-6

42.

F., Lois C., Sanchez J., et al. Kinetic evaluation and assessment of longitudinal changes in reference region and extracerebral [18F]MK-6240 PET uptake. J Cereb Blood Flow Metab, 2023; 43(4):581-594; doi: 10.1177/0271678X221142139

43.

Bittar

, Bhatt

, Hasan

F., et al. Neurotoxic tau oligomers after single versus repetitive mild traumatic brain injury. Brain Commun, 2019; 1(1):1–15; doi: 10.1093/braincomms/fcz004

44.

Michiels

, Thijs

, Mertens

, et al. In vivo detection of neurofibrillary tangles by 18F-MK-6240 PET / MR in patients with ischemic stroke. Neurology, 2023; 100(1):e62–e71; doi: 10.1212/WNL.0000000000201344

45.

Vanhaute

, Ceccarini

, Michiels

, et al. In vivo synaptic density loss is related to tau deposition in amnestic mild cognitive impairment. Neurology, 2020; 95(5):e545–e553; doi: 10.1212/WNL.0000000000009818

46.

Pascoal

A., Therriault J., Benedet A. L., et al. 18F-MK-6240 PET for early and late detection of neurofibrillary tangles. Brain, 2020; 143(9):2818–2830; doi: 10.1093/BRAIN/AWAA180

47.

Wolters

E., Ossenkoppele R., Golla S. S., et al. Hippocampal [18F]flortaucipir BPND corrected for possible spill-in of the choroid plexus retains strong clinico-pathological relationships. Neuroimage (Amst). 2020; 25:02113; doi: 10.1016/J.NICL.2019.102113

48.

Vanderlinden

, Ceccarini

, Casteele

Vande, et al. Spatial decrease of synaptic density in amnestic mild cognitive impairment follows the tau build-up pattern. Mol. Psychiatry, 2022; 27(10):4244–4251; doi: 10.1038/s41380-022-01672-x

49.

Jansen

J., Ossenkoppele R., Knol D. L., et al. Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis. JAMA Neurol, 2015; 313(19):1924–1938; doi: 10.1001/JAMA.2015.4668

50.

Therriault

, Benedet

L., Pascoal T. A., et al. APOEɛ4 potentiates the relationship between amyloid-β and tau pathologies. Mol Psychiatry, 2021; 26(10):5977–5988; doi: 10.1038/s41380-020-0688-6

51.

Stern

A., Daneshvar D. H., Baugh C. M., et al. Clinical presentation of chronic traumatic encephalopathy. Neurology, 2013; 81:1122–1129; doi: 10.1212/WNL.0b013e3182a55f7f

52.

Maroon

C., Winkelman R., Bost J., et al. Chronic traumatic encephalopathy in contact sports: A systematic review of all reported pathological cases. PLoS One, 2015; 10 (2):e0117338; doi: 10.1371/journal.pone.0117338

53.

Kolinger

D., Vállez García D., Lohith T. G., et al. A dual-time-window protocol to reduce acquisition time of dynamic tau PET imaging using [18F]MK-6240. EJNMMI Res, 2021; 11(1):49; doi: 10.1186/S13550-021-00790-X

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.01 MB

0.02 MB

Tau Imaging in Late Traumatic Brain Injury: A [ 18 F]MK-6240 Positron Emission Tomography Study

Abstract

Introduction

Methods

Participants

Data acquisition

PET analysis

Statistical analysis

Results

Participants and neuropsychological evaluations

[ 18 F]MK-6240 in individual patients with TBI

[ 18 F]MK-6240 SUVR in TBI versus HC

Association with time since injury and clinical outcome

Discussion

Conclusion

Transparency, Rigor, and Reproducibility Summary

Footnotes

Acknowledgments

Authors' Contributions

Funding Information

Author Disclosure Statement

Supplementary Material

References

Supplementary Material

[¹⁸F]MK-6240 in individual patients with TBI

[¹⁸F]MK-6240 SUVR in TBI versus HC