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緊繃型頭痛不只是肩頸緊或壓力大。2025 年 iScience 研究發現，緊繃型頭痛合併認知下降可能與海馬迴功能連結改變有關。本文從頭痛、腦霧、記憶力與中醫辨證角度，解析長期頭痛為什麼不能只靠止痛藥。
緊繃型頭痛不只是肩頸緊？研究發現：長期頭痛可能牽動海馬迴與記憶力
緊繃型頭痛不只是肩頸緊？研究發現：長期頭痛可能牽動海馬迴與記憶力
很多人以為「緊繃型頭痛」只是肩頸太硬、壓力太大、睡不好造成的日常小毛病。
尤其是上班族、照顧者、長時間用電腦的人，常常會出現一種熟悉的感覺：
頭像被一圈帶子勒住、後腦勺緊緊的、太陽穴悶痛，肩頸硬到像石頭。
吃止痛藥好像會好一點，但過幾天又回來。
更麻煩的是，頭痛久了之後，很多人會開始覺得：
「我怎麼最近比較容易忘東忘西？」
「開會時注意力變差，腦袋像有一層霧。」
「明明沒有熬夜，卻覺得思考速度變慢。」
過去這些症狀常被解釋成壓力大、睡眠差、年紀到了。但 2025 年發表於 iScience 的一篇研究提醒我們：緊繃型頭痛可能不只是肌肉緊繃，而是可能和大腦中負責記憶、疼痛調節與認知功能的「海馬迴功能連結」有關。這篇研究題為 Cognitive impairment in tension-type headache is associated with altered hippocampal functional connectivity，收錄於 iScience 第 28 卷第 11 期，文章編號 113850。
什麼是緊繃型頭痛？為什麼很多人忽略它？
什麼是緊繃型頭痛？為什麼很多人忽略它？
緊繃型頭痛，英文稱為 tension-type headache，簡稱 TTH，是非常常見的原發性頭痛類型。它通常不像偏頭痛那樣劇烈，也不一定會伴隨明顯噁心、畏光、畏聲，因此很多人會覺得「忍一下就好」。但它的麻煩之處在於：它常常反覆發生，和壓力、睡眠、肌肉緊張、情緒狀態、疼痛敏感化都有關。
典型的緊繃型頭痛，常被形容成「頭被箍住」、「頭皮緊」、「後頸緊到頭痛」、「整個頭悶悶重重」。疼痛程度多半是輕到中度，但因為發生頻率高，長期下來會嚴重影響工作效率、生活品質與情緒穩定。
近年越來越多神經科研究認為，緊繃型頭痛不能只看成單純肌肉問題。它涉及周邊肌肉張力、肌筋膜觸發點、壓力反應，也可能牽涉中樞疼痛處理系統。2025 年關於緊繃型頭痛的綜述指出，緊繃型頭痛背後是周邊機制、中樞敏感化、心理壓力與情緒共病交互作用的結果，而整合藥物、非藥物治療、生活型態調整與病人教育，對治療很重要。
換句話說，緊繃型頭痛不是「頭殼太硬」而已，而是整個身體與大腦壓力系統長期卡住的結果。
這篇研究最重要的發現：頭痛可能影響海馬迴功能連結
這篇研究最重要的發現：頭痛可能影響海馬迴功能連結
這篇 iScience 研究共納入 75 位受試者，使用 MMSE 與 MoCA 兩種常見認知評估工具來觀察認知功能，並搭配神經影像分析，特別聚焦在海馬迴與其相關區域。研究者想知道：緊繃型頭痛患者出現認知變差時，是否能在大腦功能連結上看到對應變化。
結果發現，緊繃型頭痛患者在幾個與海馬迴相關的區域出現功能連結改變，包括 subiculum、hippocampal fissure，以及左側整體海馬迴。特別值得注意的是，右側 subiculum 的功能變化，和 MoCA 認知分數以及疼痛強度之間有一致性的互動關係。
這個發現很有意思。因為海馬迴不是單純負責「記憶」而已，它也參與情緒、壓力調節、空間定位，以及疼痛經驗的整合。當疼痛長期反覆刺激大腦，大腦可能不只是「感覺到痛」，還會重新調整自己處理疼痛與認知負荷的方式。
所以這篇研究給我們一個很重要的提醒：
緊繃型頭痛合併腦霧、注意力下降、記憶變差時，不一定只是心理壓力太大，也可能反映大腦疼痛調節網路與認知網路之間的功能互相干擾。
海馬迴是什麼？為什麼它和記憶、疼痛都有關？
海馬迴是什麼？為什麼它和記憶、疼痛都有關？
海馬迴位在大腦內側顳葉，是大家熟知與記憶形成有關的重要區域。當我們把短期資訊轉換成長期記憶，或是回想某個人、某個地點、某段經驗時，海馬迴都扮演關鍵角色。
但海馬迴不是只負責「背東西」。它其實像是一個大腦的情境整合中心，會協助我們判斷：現在這個感覺代表什麼？這個環境安全嗎？這個疼痛和過去經驗有關嗎？我需要警戒嗎？
這也是為什麼長期疼痛、焦慮、睡眠障礙、慢性壓力，都可能和海馬迴功能產生關聯。疼痛不是單純從身體傳到大腦的一條線，而是會被情緒、記憶、注意力與預期感共同調節。
這篇研究特別提到的 subiculum，是海馬迴輸出訊息的重要區域之一。你可以把它想像成海馬迴對外溝通的「轉運站」。當 subiculum 的功能連結改變，可能代表海馬迴與其他腦區之間的訊息傳遞方式正在改變。研究發現右側 subiculum 和 MoCA 分數、疼痛強度之間存在互動，暗示疼痛處理與認知表現之間可能存在負向牽制。
白話來說就是：
大腦長期忙著處理疼痛，可能會壓縮原本用來記憶、專注、思考的資源。
頭痛久了，為什麼會覺得腦袋變鈍？
頭痛久了，為什麼會覺得腦袋變鈍？
很多慢性頭痛患者會描述一種「不是失智，但就是不清楚」的狀態。
例如記名字變慢、開會容易放空、講話講到一半忘記要說什麼、看文章要重讀好幾次。這種狀態常被稱為 brain fog，中文常翻成「腦霧」。
從這篇研究來看，腦霧可能不是想像出來的，而是疼痛系統與認知系統互相牽制的結果。研究者認為，緊繃型頭痛患者若伴隨認知功能下降，可能呈現特殊的海馬迴功能改變；這也可能代表疼痛調節和認知處理在海馬迴層面存在負面關聯。
我們可以用一個比喻來理解：
大腦像一間公司。
記憶、專注、情緒控制、工作效率，本來各自有部門在運作。
但如果每天都有「疼痛警報」響起，公司就要一直派人去處理警報。
久而久之，原本負責企劃、記憶、決策的員工被調去處理危機，工作效率自然下降。
所以慢性頭痛不是只有「痛」這麼簡單。它可能讓大腦長期處於警戒模式，讓認知資源被疼痛系統消耗掉。
緊繃型頭痛會導致失智嗎？這點要小心解讀
緊繃型頭痛會導致失智嗎？這點要小心解讀
這篇研究的摘要中提到，緊繃型頭痛患者的海馬迴疼痛調節與認知處理之間可能存在負向關聯，這種機制「可能」與患者未來失智風險增加有關。
但這裡要講清楚：
這不代表「有緊繃型頭痛就一定會失智」。
也不代表「頭痛就是阿茲海默症前兆」。
比較精準的說法是：
如果一個人長期反覆頭痛，又合併明顯腦霧、記憶下降、注意力變差、睡眠品質差、情緒焦慮或憂鬱，就不應該只把它當成單純肩頸痠痛。這可能代表大腦長期處在疼痛壓力與認知負荷交錯的狀態，需要更完整地評估。
尤其對 40 到 55 歲的族群來說，這是一個很重要的提醒。這個年齡層常常同時承受工作壓力、家庭照顧壓力、睡眠不足、荷爾蒙變化與慢性發炎問題。如果只是一直吃止痛藥，卻沒有處理睡眠、壓力、肌肉張力、腦部血流與身體發炎狀態，問題可能會反覆拖延。
為什麼商業白領特別容易中招？
為什麼商業白領特別容易中招？
緊繃型頭痛非常符合現代白領工作者的生活型態。
長時間盯螢幕、久坐、肩頸僵硬、用腦過度、睡眠不足、咖啡因依賴、情緒壓抑，都是常見誘因。
很多人白天開會、晚上回訊息，腦袋幾乎沒有真正關機。身體看起來坐在辦公室，神經系統卻像一直處在備戰狀態。這種狀態久了，肩頸肌肉會變緊，頭皮筋膜張力會增加，呼吸變淺，睡眠變差，疼痛閾值下降，大腦對痛覺也會越來越敏感。
更麻煩的是，很多高功能上班族會把這些訊號合理化：
「只是太累。」
「只是最近案子比較多。」
「只是年紀到了。」
「吃止痛藥就好。」
但如果頭痛已經開始影響記憶、專注、情緒與睡眠，就代表它不只是局部問題，而是身體正在提醒你：神經系統已經長期過載。
中醫怎麼看緊繃型頭痛？不是只有放鬆肩頸而已
中醫怎麼看緊繃型頭痛？不是只有放鬆肩頸而已
從中醫角度來看，緊繃型頭痛不會只被看成「肌肉太緊」。中醫會更重視整體辨證，包括疼痛位置、疼痛性質、發作時間、壓力狀態、睡眠品質、腸胃功能、月經狀態、怕冷怕熱、舌脈表現等。
常見可能包括幾種方向。
有些人是肝氣鬱結型。這類患者常常壓力大、情緒悶、胸悶、容易嘆氣，頭痛多在太陽穴或頭側，越忙越痛，越生氣越痛。
有些人是氣血不足型。這類人可能頭痛不一定劇烈，但容易疲倦、頭暈、臉色較白、睡眠淺、工作一久就腦袋空掉。
有些人是痰濕阻滯型。這類患者常覺得頭重、昏沉、像戴安全帽，伴隨腸胃脹、痰多、身體沉重、睡醒還是累。
有些人則是瘀血阻絡型。這類頭痛位置固定，可能刺痛、久痛不癒，常和長期姿勢不良、外傷、慢性循環不佳有關。
如果從這篇研究回來看，中醫治療緊繃型頭痛的價值，不只是「止痛」，而是協助身體從長期緊繃、疼痛敏感、睡眠不深、氣血失調的狀態中慢慢恢復。也就是說，中醫處理的是「為什麼你的大腦一直收到疼痛警報」。
頭痛合併腦霧，要注意哪些警訊？
頭痛合併腦霧，要注意哪些警訊？
如果只是偶爾壓力大頭痛，休息後就改善，通常不一定需要過度擔心。
但如果出現以下情況，就建議進一步評估：
第一，頭痛頻率越來越高，甚至一週好幾次。
第二，止痛藥越吃越頻繁，但效果越來越差。
第三，頭痛伴隨明顯腦霧、記憶下降、注意力變差。
第四，睡眠品質明顯變差，醒來仍然疲倦。
第五，頭痛伴隨情緒焦慮、憂鬱、易怒。
第六，出現突然劇烈頭痛、噁心嘔吐、單側無力、視力改變、說話不清等紅旗症狀。
尤其最後一類紅旗症狀，不能當成一般緊繃型頭痛處理，應該儘快就醫排除腦血管、腦壓或其他神經急症。
治療緊繃型頭痛，不能只問「哪個止痛藥最強」
治療緊繃型頭痛，不能只問「哪個止痛藥最強」
很多人治療頭痛時，第一個問題是：「哪一種止痛藥比較有效？」
但從這篇研究帶來的啟發來看，更重要的問題可能是：
為什麼我的大腦一直處在疼痛模式？
為什麼頭痛開始影響我的專注力？
為什麼睡了還是累？
為什麼肩頸放鬆一下很快又緊回來？
如果只靠止痛藥，可能暫時壓下疼痛訊號，卻沒有處理背後的壓力系統、睡眠問題、肌肉筋膜張力、情緒負荷與體質狀態。這也是為什麼很多緊繃型頭痛患者會反覆發作。
比較完整的方向應該包括：
調整睡眠節律、減少長時間固定姿勢、改善肩頸與呼吸模式、降低慢性壓力、處理腸胃與痰濕問題、改善氣血循環，必要時搭配中醫辨證治療。
這篇研究給我們的臨床提醒：頭痛是大腦壓力系統的訊號
這篇研究給我們的臨床提醒：頭痛是大腦壓力系統的訊號
這篇 iScience 研究最有價值的地方，不是告訴大家緊繃型頭痛有多可怕，而是提醒我們：頭痛不一定只是局部疼痛，它可能反映大腦疼痛調節與認知功能之間的互動。
研究納入 75 位受試者，使用 MMSE、MoCA 與神經影像分析，發現緊繃型頭痛患者的海馬迴相關功能連結出現變化，尤其右側 subiculum 與認知分數和疼痛強度之間有關。這支持一個重要概念：慢性疼痛可能會干擾大腦原本用來記憶、學習與專注的資源。
所以，當一個人長期頭痛又覺得腦袋變鈍，不該只被說成「你想太多」或「壓力大而已」。這可能是一個值得被認真看待的身體訊號。
結論：頭痛不是忍耐力測驗，而是大腦在求救
結論：頭痛不是忍耐力測驗，而是大腦在求救
緊繃型頭痛很常見，但常見不代表不重要。
它可能從肩頸緊、頭皮緊、壓力大開始，逐漸影響睡眠、情緒、專注力與記憶表現。
這篇 2025 年 iScience 研究讓我們看到，緊繃型頭痛合併認知變化時，可能和海馬迴功能連結改變有關。這不代表每個頭痛的人都會失智，但提醒我們：長期疼痛不是只有「痛」而已，它也可能改變大腦處理記憶與注意力的方式。
如果你經常頭痛、肩頸緊、睡不好，又覺得最近記憶力和專注力變差，這不是單純忍耐就會過去的問題。
你需要的可能不是更強的止痛藥，而是一次更完整的身體與神經系統評估。
頭痛，是大腦發出的警訊。
真正重要的不是把警報聲關掉，而是找出為什麼警報一直響。
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Volume 28, Issue 11, 21 November 2025, 113850
Article
Cognitive impairment in tension-type headache is associated with altered hippocampal functional connectivity
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Burak Yulug 1 2 10, Ali Yalcınkaya 2, Shair Shah Safa 1, Ayse Karakus 1, Dila Sayman 1, Seyda Cankaya 1, Ceyhun Sayman 1, Ece Ozdemir Oktem 1, Behçet Ayyildiz 3, Sevilay Ayyildiz 4 5, Uğur Aylak 6, Bernis Sutcubası 7, Ramazan Karaca 1, Mehmet Ozansoy 2, Umutcan Duran 1, Halil Aziz Velioglu 8, Lutfu Hanoglu 2, Adil Mardinoglu 9
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https://doi.org/10.1016/j.isci.2025.113850Get rights and content
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Highlights
•
The right subiculum, middle temporal, and frontal gyrus process both pain and cognition•
Hippocampal and middle temporal regions relate to cognitive decline in TTH pathology•
MoCA scores and pain intensity are negatively correlated in the TTH group
Summary
Tension-type headache (TTH) is a widespread primary headache disorder that causes mild to moderate pain, which may be seen together with cognitive deficits. It is unclear if TTH-linked cognitive impairment is associated with functional alterations. Seventy-five participants were enrolled in the study. Mini Mental State Evaluation (MMSE) and Montreal Cognition Assessment (MoCA) tests were applied to evaluate cognitive impairment. A neuroimaging analysis was applied to determine whether the hippocampus responsible for pain and cognition was affected in TTH patients. Our functional data revealed significant alterations in the connectivity of the subiculum, hippocampal fissure, and left whole hippocampus. Among the significant functional brain alterations observed, the right subiculum consistently interacted with MoCA scores and increased pain intensity. Our findings suggest that TTH patients with cognitive impairment may exhibit unique functional alterations in the hippocampus. This suggests a potential negative association between pain modulation and cognitive processes in the hippocampus that may be responsible for the increased risk of dementia in these patients.
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Subject areas
Clinical neuroscienceCognitive neuroscience
Introduction
Tension-type headache (TTH) is a ubiquitous primary headache disorder characterized by the occurrence of mild to moderate headache episodes typically manifesting as bilateral, pressing, or tightening sensations.1 Individuals with TTH frequently experience additional health issues, including fibromyalgia, temporomandibular joint dysfunction, sleep disturbances, depression, and anxiety.2,3,4,5,6 It is important to note that individuals diagnosed with TTH not only face the direct health impacts described earlier7 but also often struggle with cognitive deficits.8,9,10 However, in contrast to the substantial body of research into migraine-related cognitive and mood impairment,11,12 there is a notable scarcity of studies focused on these aspects in patients with TTH.13,14 Moreover, the limited research that does exist has produced inconsistent results. For instance, Waldie et al. found no cognitive performance difference between adult TTH patients and healthy individuals, suggesting that any cognitive issues in childhood TTH may stem from diminished educational opportunities due to the social and health impacts of the condition.13 Further investigations into the link between TTH and the risk of dementia have been inconclusive, partly due to the younger ages of study participants, limiting the applicability of the findings to the broader dementia population.15,16 Other studies have indicated that TTH patients may experience impairments in visuospatial skills, executive functions, and attention.11,14,17 However, these have not adequately considered how depression, anxiety, and insomnia might affect cognitive outcomes, making firm conclusions elusive. Collectively, these studies underscore the potential capacity of advanced neuroimaging methods to uncover the underlying brain mechanisms responsible for TTH-associated cognitive problems.18 From that perspective, several TTH studies19,20 have reported abnormal brain function and altered gray matter volume and white matter integrity,9 with some studies specifically identifying the hippocampus as a region of interest.21 However, a specific relationship between hippocampal activity and impaired cognition has not yet been shown in TTH. Regrettably, this also holds true for the functional data. While robust evidence exists for the role of regional homogeneity (ReHo) and amplitude of low-frequency fluctuations (ALFFs) in pain processing in TTH,22,23 results regarding alterations in hippocampal regions are scarce.22,23,24,25
The aforementioned discrepancy may stem from the fact that the regions identified as significant in these studies could be indicative of altered central pain processing, rather than cognition. This, in turn, makes it difficult to establish a clear differentiation between brain regions involved in cognition and pain processing. Another constraint of previous studies is the confounding effect of mood on headache. This necessitates further investigations into the mechanisms of mood in TTH in order to eliminate the potential effect of psychological distress on headache and cognition by evaluating strategic regions, such as the hippocampus, which plays a multifaceted role in mediating cognition, mood, and pain processing.26,27 This is suggested with previous studies emphasizing the role of hippocampal subregions in neurological and psychiatric diseases28 associated with cognitive impairment, such as Alzheimer’s disease (AD),29 and major depressive disorder (MDD).30 These findings are suggested with the fact that, reduced volume, decreased neurogenesis, and altered neuroplasticity in the hippocampal regions are among the morphological and functional alterations linked to depression and chronic pain mediated by a decrease in neurotrophic factors and an increase in pro-inflammatory factors.31
In light of all these gaps, a more focused approach that examines functional hippocampal alterations with appropriate adjustments is now needed to accurately delineate the cognition-related changes associated with TTH.
The study primarily aims to determine whether the functional hippocampal changes observed in TTH reflect direct cognitive impairments or are simply a byproduct of altered central pain processing and mood disturbances. Essentially, the research addresses the gap in our understanding of how TTH might lead to cognitive deficits by using advanced neuroimaging methods to clearly differentiate between regions involved in pain processing and those directly related to cognitive function. So far as we are aware, no previous integrated study has evaluated the neural correlations of a possible link between cognition and pain in TTH patients in such depth manner. Here, we conducted neuropsychological tests and seed-based resting state functional connectivity (rsFC) analyses on age-, sex- and educational years-matched healthy controls (HCs) and patients with TTH. The bilateral hippocampi were chosen as seed regions because of their important roles in cognition.
Results
Demographic features and clinical test scores
The participants’ demographic features and clinical test scores are summarized in Table 1. No significant differences were observed in terms of age (p = 0.926) and years of education (p = 0.088). However, we observed a significant difference in Montreal Cognition Assessment (MoCA) scores between TTH and control groups (Mann-Whitney U Test, p = 0.004, Table 1). MoCA scores were significantly lower in the TTH patients than in the control group (Table 1). We also found significant difference between groups in terms of Hamilton Depression Rating Scale (HDRS) scores (p < 0.001, Mann-Whitney U test, Table 1). There were no differences in terms of cognitive scores between acute and chronic type of TTH after adjusting for different HDRS and Hamilton Anxiety Scale (HAS) scores observed between the two groups (analysis of covariance, p > 0.05). The pain intensity of TTH patients (mean ± SD) was 4.34 ± 2.04, and the pain frequency (mean ± SD, days per month) was 16.6 ± 9.12.
Table 1. Demographic features and clinical test scores of the two groups are shown in the table
Empty Cell
Tension-type headache n = 29
Control n = 46
p value
Mean ± SD
Median (IQR)
Mean ± SD
Median (IQR)
Years of education
10.96 ± 4.24
12.00 (8)
12.76 ± 4.37
12.00 (4)
0.088
Age
36.51 ± 13.05
33.00 (23)
39.58 ± 18.13
32.00 (30)
0.926
MoCA
24.27 ± 2.31
24.00 (3)
26.13 ± 2.51
26.00 (4)
0.004a
MMSE
29.51 ± 0.73
30 (1)
29.28 ± 0.93
30 (1)
0.339
HDRS
9.20 ± 4.92
10 (8)
5.58 ± 3.65
6 (4.75)
<0.001a
HAS
14.10 ± 8.86
13 (13)
12.62 ± 8.17
11 (12)
0.527
Trail making part B
0.83 ± 0.39
1 (0)
0.87 ± 0.34
1 (0)
0.625
Visuospatial skills
3.62 ± 0.68
4 (1)
4.17 ± 0.68
4 (1)
<0.001a
Naming
2.58 ± 0.63
3 (1)
2.80 ± 0.40
3 (0)
0.120
Attention
5.51 ± 0.69
6 (1)
5.80 ± 0.45
6 (0)
0.025a
Sentence repetition
0.90 ± 0.90
1 (2)
1.39 ± 0.78
2 (1)
0.018a
Fluency
0.79 ± 0.41
1 (0)
0.94 ± 0.25
1 (0)
0.069
Abstraction
1.24 ± 0.74
1 (1)
1.61 ± 0.61
1 (2)
0.022a
Recall
2.76 ± 1.40
3 (2)
2.89 ± 1.54
3 (2)
0.676
Orientation
5.93 ± 0.26
6 (0)
5.96 ± 0.21
6 (0)
0.645
Gender (female n, %)
21 (%72)
25 (%54)
0.122
a
There were no significant differences in terms of age and education year between the two groups. On the other hand, MoCA and HDRS scores showed significant difference (p = 0.004, Mann-Whitney U test was performed.) Also, MoCA subtests visuospatial skills, attention, sentence repetition, and abstraction showed significant difference between groups. (Abbreviations: MoCA, Montreal Cognition Assessment; MMSE, Mini Mental State Evaluation; HDRS, Hamilton Depression Rating Scale; HAS, Hamilton Anxiety Scale).
Functional connectivity results
Functional connectivity group differences
In the analysis of functional connectivity of the hippocampus and its subfields (Figure 1) with other brain regions, the left hippocampal fissure in the TTH group showed significantly increased functional connectivity with the left lingual gyrus and left intracalcarine cortex (Figures 2 and 3; Table 2). Similarly, the right subiculum body in the TTH group showed significantly increased functional connectivity with the right cerebellum in comparison to healthy controls. Also, the left whole hippocampal connectivity showed both increased and decreased functional connectivity in the TTH group. Herein, the left superior lateral occipital cortex and left occipital pole were significantly increased whereas the right supramarginal gyrus and right parietal operculum cortex were decreased in the TTH group (Figures 2 and 3; Table 2).
Figure 1. The bilateral hippocampal subfield seeds
Figure 2. Functional connectivity differences of hippocampal subfields between the two groups
The connectivity of the left whole hippocampus with the right supramarginal gyrus and right parietal operculum cortex decreased, while the connectivity with the left superior lateral occipital cortex and left occipital pole significantly increased in the TTH group. The left hippocampal fissure in the TTH group showed significantly increased functional connectivity with the left lingual gyrus and intracalcarine cortex. The right subiculum body in the TTH group showed significantly increased functional connectivity with the right cerebellum in comparison to healthy controls. The regions depicted on brain maps are described in Table 2.
Figure 3. Effect size of hippocampal subregions showing functional connectivity differences between patients with TTH and control groups
The effect sizes (Fisher z-transformed correlation coefficients) are shown for two groups: the blue bars represent the control group, and the red bars represent the TTH group. The effect size reflects the strength of functional connectivity in ROIs, which shows significant connectivity differences between the two groups. The coordinates on the x axis correspond to the peak MNI coordinates (from Table 2), related to the ROIs that exhibited functional connectivity differences between the two groups.
Table 2. Functional connectivity differences between the two groups
Seeds
Cluster locations
Cluster coordinates
Size
t-max
p value
TTH > Control
Left hippocampus
left superior lateral occipital cortex, left occipital pole
−20−84 + 44
139
4.93
0.023516
Left hippocampal fissure
left lingual gyrus, left intracalcarine cortex
−12−74 -02
114
4.85
0.036351
Right subiculum body
right cerebellum
+26–64−58
120
5.25
0.024624
Left hippocampus
right supramarginal gyrus, right parietal operculum cortex
+58−34+34
149
−5.18
0.016247
The table shows the seeds exhibiting functional connectivity differences between the TTH group and the control group. The left hippocampal fissure and right subiculum show increased connectivity in the TTH group. Additionally, the left whole hippocampus shows both increased and decreased connectivity in the TTH group. The t-values reported in the table correspond to the post hoc comparisons between the groups. The p value reflects differences between groups, assessed using one-way analyses of covariance (ANCOVA), with control variables taken into account. (Voxel-wise significance was set at p < 0.001, and cluster-level significance was determined with p FWE < 0.05.).
Functional connectivity regression analysis
Among the functionally significant regions, the right subiculum’s connectivity with the left postcentral gyrus and left supramarginal gyrus was significantly interacted with the MoCA scores. (Figures 4 and 5; Table 3).
Figure 4. Functional connectivity in the TTH group, analyzed based on the interaction between MoCA scores and pain intensity
The connectivity between the right subiculum and left postcentral gyrus and left supramarginal gyrus correlates negatively with pain intensity depending on MoCA scores. The regions depicted on brain map are described in Table 3.
Figure 5. Effect size of ROI showing significant functional connectivity in the TTH group based on the interaction between MoCA scores and pain intensity
The effect size reflects the strength of functional connectivity in ROI that shows significant connectivity differences based on the pain intensity ∗ MoCA interaction in the TTH group.
Table 3. Functional connectivity in the TTH group, analyzed based on the interaction between MoCA scores and pain intensity
Seeds
Cluster locations
Cluster coordinates
Size
t-max
p value
R
Negative correlation with pain intensity
Right subiculum
left postcentral gyrus, left supramarginal gyrus
−46−24+30
60
−6.28
0.039898
−0.442
Specifically, the connectivity between the right subiculum and the left postcentral gyrus, along with the left supramarginal gyrus, negatively correlates with pain intensity depending on MoCA scores.
Discussion
In this study, we found that, compared with healthy controls, patients with TTH exhibited impaired cognition and increased anxiety, which showed lower MoCA scores and higher HAS and HDRS scores. The rsFC features of left hippocampus and hippocampal subfields with the left occipital cortex, left lingual gyrus, and right cerebellum were significantly different in TTH patients compared to HCs. Moreover, the rsFC strength of the subiculum with the right cerebellum was associated with impaired cognition and increased headache frequency in TTH patients. Although participants with TTH exhibited significantly reduced cognitive performance compared to the healthy controls, no significant difference was observed in terms of cognitive scores between acute and chronic types of headache patients.
Although our findings contradict recent findings by Wang et al., showing no significant differences in hippocampal connectivity between TTH and healthy controls, it suggests previous structural studies indicating the role of hippocampus in adult patients with high-frequency TTH.32 In the light of the existing literature, which highlights the adverse impact of acute pain on cognitive function,33 our findings of functional alterations in relevant brain regions critical to cognition (such as, left hippocampus, hippocampal fissure, and subiculum) support the hypothesis that the processing of acute episodes of pain may interact negatively with cognitive functions, potentially leading to a decline in cognitive capacities. Our finding of significantly different hippocampal connectivity changes in group comparisons is also supportive of recent data concerning an increased risk of dementia in patients with TTH.8,9,10 Within this framework, the current results align with recent studies highlighting the undeniable role of the hippocampus in AD as well as during the experience of pain.34,35 An additional example supporting our findings is a recent study reporting that the enlargement of the hippocampal fissure was a prominent finding associated with medial temporal lobe atrophy in patients with AD compared to healthy individuals.36 Collectively, these results are consistent with our own observations of altered hippocampal connectivity in TTH patients with cognitive impairment, which corresponds with its known role in predicting the development of AD, particularly in early stages such as mild cognitive impairment.37
Among the aforementioned hippocampal regions identified as differing functionally significant between the TTH patients and healthy controls, the right subiculum emerged as a unique region that consistently interacted with the processing of cognition and pain. From that perspective, our present study proposes that episodes of pain, particularly in terms of intensity, may lead to increased activation in brain regions associated with the encoding of painful memories at the expense of cognitive functioning.38 This aligns with several human and animal studies,26,27,39 suggesting a significant association between reduced hippocampal volume and memory impairments in the presence of chronic pain.40,41 A good clinical example of this could be recent interesting data indicating that even acute pain may impair executive functions.33,42 This is consistent with our results showing a significant negative correlation between MoCA and pain intensity scores that uniquely interacted on the right subiculum body’s connection with the left postcentral gyrus and left supramarginal gyrus (Figures 4 and 5; Table 3).
It was, therefore, not unreasonable to assume that this cognitively relevant region might also play a mediating role in pain and cognitive impairment, a finding worthy of further discussion. For instance, O’Mara43 confirmed the role of the subiculum in spatial information processing, memory, stress responses, and motivated behavior, which are all relevant to various neurocognitive processes. This aligns well with a novel study by Gao et al. suggesting that short-term intrinsic plasticity can modulate pain-related neuronal circuits through subiculum’s unique sensitizing function on the retrosplenial cortex.44 In addition, clinically relevant data reported by Chen et al. in patients with medication-overuse headaches showed a negative correlation between headache characteristics, anxiety scores, and right subiculum activity.25 This implies that the right subiculum significantly influences the pathophysiology of such headaches. Confirming the role of the subiculum in pain, another study reported that trigeminal neuralgia was associated with decreased right subiculum activity compared to a control group.45
Although we specified the presence of depression as an exclusion criterion, we observed higher subclinical HDRS scores in the TTH patients than in the control group, which might represent a possible confounding factor affecting our results. In order to eliminate the impact of subtle depressive symptoms on cognition, we therefore adjusted HDRS scores in group difference functional connectivity analysis.
In conclusion, our present findings suggest that TTH patients with cognitive impairment may exhibit unique functional alterations in brain regions, as confirmed with functional analyses. This suggests a potential negative association between pain modulation and cognitive processes in these regions that may be responsible for the increased risk of dementia in these patients and may pave the way for new therapeutic strategies to address cognitive deficits in TTH patients.
Limitations of the study
Limitations of this study also include its cross-sectional nature, which makes it difficult to infer causal and temporal relationships. Furthermore, functional and structural MRI acquisitions were limited in spatial resolution, and low-quality signal effects may have biased results. To minimize potential confounding by signal heterogeneity in our analyses, we applied a quality check of the regions of interest. Finally, due to the cross-sectional design of our study, we cannot draw conclusions regarding the order of pathological events driving subregional brain changes, which should be addressed in ongoing longitudinal studies. It is also important to mention that the findings of this study should be interpreted cautiously and irrespective of direction of connectivity, especially in terms of identifying brain regions that are likely to contribute to cognitive impairment, especially in pain conditions such TTH.46
Resource availability
Lead contact
Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Burak Yulug (burak.yulug@alanya.edu.tr).
Materials availability
This study did not generate new unique reagents.
Data and code availability
•
Data: Data are available on request due to privacy/ethical restrictions. Further information for data should be directed to and will be fulfilled by the lead contact, Burak Yulug (burak.yulug@alanya.edu.tr).•
Code: This study did not use any custom or publicly available code.
Acknowledgments
All authors would like to thank all participants who took part in this study. No funding support was received from any institution for this work.
Author contributions
Conceptualization: B.Y. and S.S.S. Formal analysis: U.D., H.A.V., L.H., and A.M. Investigation: B.Y., H.A.V., S.C., and A.K. Methodology: B.Y. and S.S.S. Project administration: B.Y., S.S.S., B.S., and U.A. Software: B.A., S.A., and A.Y. Supervision: B.Y., S.S.S., H.A.V., L.H., and A.M. Validation: S.C., R.K., C.S., and M.O. Visualization: C.S., D.S., E.O.O., and M.O. Writing – original draft: B.Y., S.S.S., S.C., and A.K. Writing – review and editing: B.Y., H.A.V., L.H., A.M., and S.C.
Declaration of interests
The authors declare no competing interests.
STAR★Methods
Key resources table
REAGENT or RESOURCE
SOURCE
IDENTIFIER
Software and algorithms
MRIcroGL
https://www.nitrc.org/projects/mricrogl
Version 1.2
CONN functional connectivity toolbox
Version 21a
MATLAB
MathWorks
R2023a
SPM12 toolbox
https://www.fil.ion.ucl.ac.uk/spm/software/spm12
–
ART (Artifact Detection Tool)
Included in CONN toolbox
–
FreeSurfer
https://surfer.nmr.mgh.harvard.edu
Version 7.1.1
Statistical analysis (ANOVA, GLM)
Conducted with CONN and MATLAB
–
G∗Power
https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower
Version 3.1
Jamovi
Version 2.3.19.0
Experimental model and study participant details
Seventy-five (29 TTH patients (14 episodic and 15 chronic) and 46 healthy controls) participants were enrolled in the study. 21 (%72) participants of the TTH group and 25 (%54) participants of the control group were female. The participants were recruited from the Alanya Alaaddin Keykubat University, Department of Neurology and Clinical Neurosciences, Antalya/Turkey. Participants with neurodegenerative, neuropsychiatric chronic metabolic disease, or previous histories of trauma and any other headache types were excluded from the study. The tension-type headache diagnosis was done by an experienced neurologist based on International Headache Committee diagnostic criteria. All evaluations of the control group were performed during the study, and the national general health system was employed to verify that none of the control group members had ever been diagnosed with tension-type headache or any other headache type, such as migraine. The Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were applied to evaluate the cognitive impairment of the participants, while the MMSE test was used to rule out dementia. All subjects with tension-type headaches were enrolled during the interictal period. Also, the Hamilton Depression Rating Scale (HDRS) was used to assess depression and Hamilton Anxiety Scale (HAS) was used to assess anxiety in both the TTH and control groups, and participants who were using antidepressant or anti-anxiety medications at the time of evaluation were excluded from the study. Participants were diagnosed with TTH based on the International Classification of Headache Disorders (ICHD-3) criteria. Patients' pain severity was assessed using VAS (Visual Analog Scale) scores and rated it on a scale from 0 to 10. Approval of the study was granted by the Istanbul Medipol University ethical committee (Ethical report number 16072023/289). G∗power (ver. 3.1.6.6) software was used to determine the sample size. This showed that a sample size of 16 subjects would be required for 90% power with a significance level of α=0.05.47
Consent statement
All participants provided and signed informed consent.
Method details
MRI data acquisition
Structural and resting-state fMRI were conducted using a Signa Explorer MR device (General Electric Company, USA) at Alanya Alaaddin Keykubat University. Each T1-weighted structural scan consisted of 190 slices (TR/TE: 8.1/3.7), FOV 256 x 256 x 190 mm (FHxAPxRL), and a voxel size of 1 x 1 x 1 mm. Echo-planar imaging sequences (EPI) were used to record fMRI scans in the resting state. The scanning procedure had a duration of approximately 12 minutes and 300 volumes were recorded with the following parameters: TR 2230 ms, TE 30 ms, FOV 240 x 240 x 140 mm (RLxAPxFH), voxel size 3 x 3 x 4 mm, flip angle 77°, and slice number 35. All participants were instructed to close their eyes, remain still and relaxed, clear their minds, and resist falling asleep before the scan.
Extraction of hippocampal seeds
The bilateral hippocampal subfield seeds were obtained using FreeSurfer software (version 7.1.1). The “recon-all” pipeline was utilized to segment the MNI 1-mm standard space. Following this segmentation, the hippocampus segmentation tool in FreeSurfer was used to delineate the hippocampal subfields. In the structural analysis of the hippocampus subfields, the hippocampal fissure and subiculum, which were different between the groups, were selected as seeds (Table S1). The seeds, initially in FreeSurfer’s (.mgh/.mgz) format, were converted to Nifti (.nii/.nii.gz) format and subsequently incorporated into the CONN toolbox to enable seed-based connectivity analyses (Figure 1).
Analysis of rsFC
The participants’ structural and resting-state functional images were converted from DICOM to NIfTI format using MRIcroGL (https://www.nitrc.org/projects/mricrogl) and then imported into the Functional Connectivity Toolbox (CONN v21a, https://web.conn-toolbox.org), a Matlab- and SPM-based software (https://www.fil.ion.ucl.ac.uk/spm/software/spm12/). The images underwent preprocessing using the default functional and anatomical pipeline, which includes realignment & unwarp, slice-timing correction, outlier detection, segmentation, normalization, and smoothing. Head movement realignment was set at the 97th percentile with linear motion parameters >0.9 mm and global signal z >3 thresholds. Outlier functional images were detected using the artefact rejection tool (ART).48 Functional data were smoothed with a 6 mm3 full width at half-maximum (FWHM) Gaussian kernel. The T1-weighted structural images were segmented into gray matter, white matter, and cerebrospinal fluid using tissue probability maps. Functional data were bandpass filtered at 0.008-0.1 Hz to reduce noise effects. The six motion parameters and their first-order derivatives were regressed out, and signals from the white matter and cerebrospinal fluid were collected as part of the anatomical component-based noise correction (aCompCor),49 along with other confounding factors from the resting state. Regions exhibiting significant intergroup differences in the structural analysis were subsequently selected as seeds for further analysis. Additionally, the hippocampal subfields extracted with FreeSurfer were imported into the CONN toolbox for seed-to-voxel analysis. In the first-level analysis, the average BOLD time series of all regions of interest (ROIs) were extracted, and the correlation coefficients between the BOLD time series of each ROI and brain voxel were computed. For statistical analysis, these correlation coefficients were z-transformed using the Fisher transformation. The z-values for all ROIs were compared between the two groups using analysis of variance (ANOVA) at the second level, the distribution of groups and covariances were added to the General Linear Model (GLM). Comparative analyses between groups and regression analyses were subsequently conducted within the toolbox. Finally, p < 0.001 at the voxel level and family-wise error (FWE) adjusted p < 0.05 at the cluster level.
Quality check
All participants' MRI images were reviewed by two researchers to ensure they were of sufficient quality, and it was determined that both the anatomical and functional MRI images of all participants were adequate for preprocessing. Following the preprocessing stage, another review was conducted. During this phase, the segmentation of T1-weighted anatomical images was assessed, including parameters for gray matter segmentation, white matter segmentation, and cerebrospinal fluid segmentation. The normalization of functional and anatomical images to the MNI standard template was also visually checked. Additionally, for the functional images, parameters such as the number of valid and invalid scans, maximum and average motion, and maximum and average global signal changes were evaluated. If a subject had 25% or more invalid scans of the total number of scans and exhibited extreme values in either maximum and average motion or maximum and average global signal changes, they were excluded from the study. Extreme values were defined as those exceeding the threshold of Q3 + 3 IQR (or falling below Q1 - 3 IQR in cases where extremely low values indicated data problems). Here, Q1 and Q3 represent the first and third quartiles of the measure’s distribution across the entire dataset, respectively, and IQR is the interquartile range, which is the difference between Q3 and Q1.50
Quantification and statistical analysis
Statistical analysis
Simple descriptive statistics and cognitive scores were analyzed using Jamovi software (version 2.3.19.0). The Shapiro-Wilk test was used to check the normality of the variables. Continuous variables are presented as mean ± standard deviation (mean ± SD) and median (IQR) and categorical variables as frequency (n) and percentage (%). The Independent T-Test and Mann-Whitney U tests were used to analyze MOCA differences between the groups. A two-sided p-value ≤ 0.05 was interpreted as statistically significant.
Supplemental information
Download: Download Acrobat PDF file (68KB)
Document S1. Table S1.
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