Repository: neurostuff/NiMARE

Issue:
sample_size data being ignored.
## Summary
<!--What is the nature of the bug?-->
sample size metadate from the study is being ignored

## Additional details
<!--Please fill in the following details-->
- NiMARE version: 0.5.3

### What were you trying to do?

### What did you expect to happen?

### What actually happened?

## Reproducing the bug
<!--Please share any steps you performed that revealed the bug-->
<!--Please include any code snippets.
Enclose them in triple back-ticks (```)
Like this:

```
<code>
```
-->


Discussion:
sourcery-ai[bot]: <!-- Generated by sourcery-ai[bot]: start review_guide -->

🧙 Sourcery is reviewing your pull request!

---

<details>
<summary>Tips and commands</summary>

#### Interacting with Sourcery

- **Trigger a new review:** Comment `@sourcery-ai review` on the pull request.
- **Continue discussions:** Reply directly to Sourcery's review comments.
- **Generate a GitHub issue from a review comment:** Ask Sourcery to create an
  issue from a review comment by replying to it. You can also reply to a
  review comment with `@sourcery-ai issue` to create an issue from it.
- **Generate a pull request title:** Write `@sourcery-ai` anywhere in the pull
  request title to generate a title at any time. You can also comment
  `@sourcery-ai title` on the pull request to (re-)generate the title at any time.
- **Generate a pull request summary:** Write `@sourcery-ai summary` anywhere in
  the pull request body to generate a PR summary at any time exactly where you
  want it. You can also comment `@sourcery-ai summary` on the pull request to
  (re-)generate the summary at any time.
- **Generate reviewer's guide:** Comment `@sourcery-ai guide` on the pull
  request to (re-)generate the reviewer's guide at any time.
- **Resolve all Sourcery comments:** Comment `@sourcery-ai resolve` on the
  pull request to resolve all Sourcery comments. Useful if you've already
  addressed all the comments and don't want to see them anymore.
- **Dismiss all Sourcery reviews:** Comment `@sourcery-ai dismiss` on the pull
  request to dismiss all existing Sourcery reviews. Especially useful if you
  want to start fresh with a new review - don't forget to comment
  `@sourcery-ai review` to trigger a new review!

#### Customizing Your Experience

Access your [dashboard](https://app.sourcery.ai) to:
- Enable or disable review features such as the Sourcery-generated pull request
  summary, the reviewer's guide, and others.
- Change the review language.
- Add, remove or edit custom review instructions.
- Adjust other review settings.

#### Getting Help

- [Contact our support team](mailto:support@sourcery.ai) for questions or feedback.
- Visit our [documentation](https://docs.sourcery.ai) for detailed guides and information.
- Keep in touch with the Sourcery team by following us on [X/Twitter](https://x.com/SourceryAI), [LinkedIn](https://www.linkedin.com/company/sourcery-ai/) or [GitHub](https://github.com/sourcery-ai).

</details>

<!-- Generated by sourcery-ai[bot]: end review_guide -->

Fix (patch):
diff --git a/nimare/io.py b/nimare/io.py
index d76f063..1d93cd1 100644
--- a/nimare/io.py
+++ b/nimare/io.py
@@ -63,8 +63,12 @@ def convert_nimads_to_dataset(studyset, annotation=None):
             },
         }
 
-        sample_sizes = analysis.metadata.get("sample_sizes", None)
-        sample_size = analysis.metadata.get("sample_size", None)
+        sample_sizes = analysis.metadata.get("sample_sizes", None) or study.metadata.get(
+            "sample_sizes", None
+        )
+        sample_size = analysis.metadata.get("sample_size", None) or study.metadata.get(
+            "sample_size", None
+        )
 
         # Validate sample sizes if present
         if sample_sizes is not None and not isinstance(sample_sizes, (list, tuple)):


Test changes:
diff --git a/nimare/tests/conftest.py b/nimare/tests/conftest.py
index 93a539f..40b87ec 100644
--- a/nimare/tests/conftest.py
+++ b/nimare/tests/conftest.py
@@ -172,6 +172,20 @@ def testdata_ibma_resample(tmp_path_factory):
     return dset
 
 
+@pytest.fixture(scope="session")
+def sample_size_nimads_studyset():
+    """Download/lookup example NiMADS studyset."""
+    out_file = os.path.join(get_test_data_path(), "sample_size_nimads_studyset.json")
+    if not os.path.isfile(out_file):
+        url = "https://neurostore.org/api/studysets/zvE8LEQHAJxV?nested=true"
+        response = request("GET", url)
+        with open(out_file, "wb") as f:
+            f.write(response.content)
+    with open(out_file, "r") as f:
+        studyset = json.load(f)
+    return studyset
+
+
 @pytest.fixture(scope="session")
 def example_nimads_studyset():
     """Download/lookup example NiMADS studyset."""
diff --git a/nimare/tests/data/sample_size_nimads_studyset.json b/nimare/tests/data/sample_size_nimads_studyset.json
new file mode 100644
index 0000000..2121126
--- /dev/null
+++ b/nimare/tests/data/sample_size_nimads_studyset.json
@@ -0,0 +1,1 @@
+{"id":"zvE8LEQHAJxV","name":"Studyset for Identification of a common neural familial vulnerability pattern for psychiatric illness: A systematic review and ALE meta-analysis","user":"auth0|66546ba3a94038ae9812fce4","description":"VBM New","publication":null,"doi":null,"pmid":null,"created_at":"2025-06-15T13:15:30.921734+00:00","updated_at":null,"studies":[{"id":"3AoEQXKk7oen","created_at":"2025-05-17T14:09:07.542552+00:00","updated_at":"2025-06-15T13:59:49.810128+00:00","user":"auth0|66546ba3a94038ae9812fce4","name":"Dissociable brain structural changes associated with predisposition, resilience, and disease expression in bipolar disorder.","description":"\nGenetic factors are important in the etiology of bipolar disorder (BD). However, first-degree relatives of BD patients are at risk for a number of psychiatric conditions, most commonly major depressive disorder (MDD), although the majority remain well. The purpose of the present study was to identify potential brain structural correlates for risk and resilience to mood disorders in patients with BD, type I (BD-I) and their relatives. Structural magnetic resonance imaging scans were acquired from 30 patients with BD-I, 50 of their first-degree relatives (28 had no Axis I disorder, while 14 had MDD) and 52 controls. We used voxel-based morphometry, implemented in SPM5 to identify group differences in regional gray matter volume. From the identified clusters, potential differences were further examined based on diagnostic status (BD-I patients, MDD relatives, healthy relatives, controls). Whole-brain voxel-based analysis identified group differences in the left hemisphere in the insula, cerebellum, and substantia nigra. Increased left insula volume was associated with genetic preposition to BD-I independent of clinical phenotype. In contrast, increased left substantia nigra volume was observed in those with the clinical phenotype of BD-I. Changes uniquely associated with the absence of a clinical diagnosis in BD relatives were observed in the left cerebellum. Our data suggest that in BD, genetic and phenotype-related influences on brain structure are dissociable; if replicated, these findings may help with early identification of high-risk individuals who are more likely to transition to syndromal states.","publication":"The Journal of neuroscience : the official journal of the Society for Neuroscience","doi":"10.1523/JNEUROSCI.2204-09.2009","pmid":"19726644","authors":"Matthew J Kempton, Morgan Haldane, Jigar Jogia, Paul M Grasby, David Collier, Sophia Frangou","year":2009,"metadata":{"sample_size":null},"source":null,"source_id":null,"source_updated_at":null,"analyses":[]},{"id":"X7NRDxyq7xiy","created_at":"2025-05-17T14:36:59.037807+00:00","updated_at":"2025-07-31T08:35:30.745121+00:00","user":"auth0|66546ba3a94038ae9812fce4","name":"A Multi-Dimensional and Integrative Approach to Examining the High-Risk and Ultra-High-Risk Stages of Bipolar Disorder","description":" \n## Background \n  \nValidating the high-risk (HR) and ultra-high-risk (UHR) stages of bipolar disorder (BP) may help enable early intervention strategies. \n\n\n## Methods \n  \nWe followed up with 44 offspring of parents with BP, subdividing into the HR and UHR categories. The offspring were aged 8–28 years and were free of any current DSM-IV diagnoses. Our multilevel, integrative approach encompassed gray matter (GM) volumes, brain network connectivity, neuropsychological performance, and clinical outcomes. \n\n\n## Findings \n  \nCompared with the healthy controls (HCs) (n = 33), the HR offspring (n = 26) showed GM volume reductions in the right orbitofrontal cortex. Compared with the HR offspring, the UHR offspring (n = 18) exhibited increased GM volumes in four regions. Both the HR and UHR offspring displayed abnormalities in the inferior occipital cortex regarding the measures of degree and centrality, reflecting the connections and roles of the region, respectively. In the UHR versus the HR offspring, the UHR offspring exhibited upwards-shifted small world topologies that reflect high clustering and efficiency in the brain networks. Compared with the HCs, the UHR offspring had significantly lower assortativity, which was suggestive of vulnerability. Finally, processing speed, visual–spatial, and general function were impaired in the UHR offspring but not in the HR offspring. \n\n\n## Interpretation \n  \nThe abnormalities observed in the HR offspring appear to be inherited, whereas those associated with the UHR offspring represent stage-specific changes predisposing them to developing the disorder. \n\n   Highlights  \n  \nPathophysiological alterations were identified in the high-risk and ultra-high-risk stages of bipolar disorder (BP). \n  \nDeficits in processing speed and visual-spatial memory were observed in the ultra-high-risk stage of BP. \n  \nAbnormalities associated with the ultra-high-risk stage are suggestive of risk for the full development of BP. \n  \nOur data support that the underlying abnormalities of BP may become apparent long before the official onset. \n  \nIdentifying the early development of BP opens the avenue for early intervention. \n  \n ","publication":"EBioMedicine","doi":"10.1016/j.ebiom.2015.06.027","pmid":"26425699","authors":"Lin K; Xu G; Wong NM; Wu H; Li T; Lu W; Chen K; Chen X; Lai B; Zhong L; So KF; Lee TM","year":2015,"metadata":{"sample_size":59,"sample_sizes":null},"source":"neurostore","source_id":"xkrHcD45vnWU","source_updated_at":"2024-06-17T22:30:04.291708+00:00","analyses":[{"id":"7GmAU8yUEkmR","user":"auth0|66546ba3a94038ae9812fce4","name":"Strict ","metadata":null,"description":"Increases","conditions":[],"weights":[],"points":[{"id":"da8htb2yrgQS","coordinates":[8.0,-54.0,-6.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.41}]},{"id":"r829MHppYQDe","coordinates":[6.0,62.0,-20.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.1}]}],"images":[]}]},{"id":"8uhr6pioZtfv","created_at":"2025-06-15T13:15:31.980769+00:00","updated_at":"2025-06-15T13:55:29.381850+00:00","user":"auth0|66546ba3a94038ae9812fce4","name":"Decreased hippocampal volume in healthy girls at risk of depression.","description":"CONTEXT\nResearchers have documented that the hippocampus is smaller in individuals with depression than in those without. The temporal or causal association of this reduction in hippocampal volume in depression, however, is not known.\nOBJECTIVE\nTo test the hypothesis that reduced hippocampal volume precedes and therefore may be implicated in the onset of depression.\nDESIGN\nWe used magnetic resonance imaging to examine brain structure volume in individuals at high and low familial risk of depression. Anatomic images from magnetic resonance imaging were analyzed using both whole-brain voxel-based morphometry and manual tracing of the bilateral hippocampus.\nSETTING\nA research university.\nPARTICIPANTS\nFifty-five girls aged between 9 and 15 years: 23 daughters of mothers with recurrent episodes of depression in the daughter's lifetime (high risk) and 32 age-matched daughters of mothers with no history of psychopathology (low risk). None of the girls had any past or current Axis I psychopathology.\nMAIN OUTCOME MEASURES\nGroup differences in voxel-based morphometry brain matter density estimates and traced hippocampal volume.\nRESULTS\nVoxel-based morphometry analyses indicated that individuals at high risk of depression had significantly less gray matter density in clusters in the bilateral hippocampus (P < .001) than low-risk participants. Tracing yielded a volumetric reduction in the left hippocampus in the high-risk participants (P < .05).\nCONCLUSIONS\nCompared with individuals at low familial risk of the development of depression, high-risk individuals have reduced hippocampal volume, indicating that neuroanatomic anomalies associated with depression may precede the onset of a depressive episode and influence the development and course of this disorder.\n","publication":"Archives of general psychiatry","doi":"10.1001/archgenpsychiatry.2009.202","pmid":"20194827","authors":"Michael C Chen, J Paul Hamilton, Ian H Gotlib","year":2010,"metadata":{"sample_size":null},"source":null,"source_id":null,"source_updated_at":null,"analyses":[]},{"id":"jXxsukxhMaP5","created_at":"2025-05-17T14:19:13.090754+00:00","updated_at":"2025-06-18T18:08:50.370630+00:00","user":"auth0|66546ba3a94038ae9812fce4","name":"Brain regions associated with risk and resistance for bipolar I disorder: a voxel-based MRI study of patients with bipolar disorder and their healthy siblings.","description":"OBJECTIVE: Bipolar I disorder is a highly heritable disorder but not all siblings manifest with the illness, even though they may share similar genetic and environmental risk factors. Thus, sibling studies may help to identify brain structural endophenotypes associated with risk and resistance for the disorder., METHODS: Structural magnetic resonance imaging (MRI) scans were acquired for 28 euthymic patients with bipolar disorder, their healthy siblings, and 30 unrelated healthy controls. Statistical Parametric Mapping 8 (SPM8) was used to identify group differences in regional gray matter volume by voxel-based morphometry (VBM)., RESULTS: Using analysis of covariance, gray matter analysis of the groups revealed a group effect indicating that the left orbitofrontal cortex [Brodmann area (BA) 11] was smaller in patients with bipolar disorder than in unrelated healthy controls [F = 14.83, p < 0.05 (family-wise error); 7 mm(3) ]. Paired t-tests indicated that the orbitofrontal cortex of patients with bipolar disorder [t = 5.19, p < 0.05 (family-wise error); 37 mm(3) ] and their healthy siblings [t = 3.89, p < 0.001 (uncorrected); 63 mm(3) ] was smaller than in unrelated healthy controls, and that the left dorsolateral prefrontal cortex was larger in healthy siblings than in patients with bipolar disorder [t = 4.28, p < 0.001 (uncorrected); 323 mm(3) ] and unrelated healthy controls [t = 4.36, p < 0.001 (uncorrected); 245 mm(3) ]. Additional region-of-interest analyses also found volume deficits in the right cerebellum of patients with bipolar disorder [t = 3.92, p < 0.001 (uncorrected); 178 mm(3) ] and their healthy siblings [t = 4.23, p < 0.001 (uncorrected); 489 mm(3) ], and in the left precentral gyrus of patients with bipolar disorder [t = 3.61, p < 0.001 (uncorrected); 115 mm(3) ] compared to unrelated healthy controls., CONCLUSIONS: The results of this study suggest that a reduction in the volume of the orbitofrontal cortex, which plays a role in the automatic regulation of emotions and is a part of the medial prefrontal network, is associated with the heritability of bipolar disorder. Conversely, increased dorsolateral prefrontal cortex volume may be a neural marker of a resistance factor as it is part of a network of voluntary emotion regulation and balances the effects of the disrupted automatic emotion regulation system. Copyright © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.","publication":"Bipolar disorders","doi":"10.1111/bdi.12181","pmid":"24589068","authors":"Cagdas Eker, Fatma Simsek, Evrim Ebru Yilmazer, Omer Kitis, Cem Cinar, Ozlem Donat Eker, Kerry Coburn, Ali Saffet Gonul","year":2014,"metadata":{"Sample_size":58},"source":"neurostore","source_id":"uHHBpcAyQfEh","source_updated_at":"2025-04-17T18:41:20.055251+00:00","analyses":[]},{"id":"pyrzepjmEux4","created_at":"2025-06-15T13:39:20.553689+00:00","updated_at":"2025-07-31T08:35:42.753768+00:00","user":"auth0|66546ba3a94038ae9812fce4","name":"Cognitive correlates of gray matter abnormalities in adolescent siblings of patients with childhood-onset schizophrenia","description":"Patients with childhood onset schizophrenia (COS) display widespread gray matter (GM) structural brain abnormalities. Healthy siblings of COS patients share some of these structural abnormalities, suggesting that GM abnormalities are endophenotypes for schizophrenia. Another possible endophenotype for schizophrenia that has been relatively unexplored is corticostriatal dysfunction. The corticostriatal system plays an important role in skill learning. Our previous studies have demonstrated corticostriatal dysfunction in COS siblings with a profound skill learning deficit and abnormal pattern of brain activation during skill learning. This study investigated whether structural abnormalities measured using volumetric brain morphometry (VBM) were present in siblings of COS patients and whether these were related to deficits in cognitive skill learning. Results revealed smaller GM volume in COS siblings relative to controls in a number of regions, including occipital, parietal, and subcortical regions including the striatum, and greater GM volume relative to controls in several subcortical regions. Volume in the right superior frontal gyrus and cerebellum were related to performance differences between groups on the weather prediction task, a measure of cognitive skill learning. Our results support the idea that corticostriatal and cerebellar impairment in unaffected siblings of COS patients are behaviorally relevant and may reflect genetic risk for schizophrenia.","publication":"Schizophrenia research","doi":null,"pmid":"25541139","authors":"Wagshal D, Knowlton BJ, Cohen JR, Bookheimer SY, Bilder RM, Fernandez VG, Asarnow RF","year":2015,"metadata":{"sample_size":61,"sample_sizes":null},"source":"neurostore","source_id":"7KNm6hoLrqG4","source_updated_at":"2024-03-21T20:01:55.285477+00:00","analyses":[{"id":"rzx6wJdpAoCU","user":"auth0|66546ba3a94038ae9812fce4","name":"Lenient","metadata":null,"description":"Increases","conditions":[],"weights":[],"points":[{"id":"FYd3MfRDHeoE","coordinates":[16.0,-24.0,-34.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":3.51}]},{"id":"K9TGkRLz63QX","coordinates":[14.0,-64.0,30.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":3.861}]},{"id":"N4kSSwyojDzh","coordinates":[-12.0,-88.0,24.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":3.829}]},{"id":"QvYnfwtkGXuB","coordinates":[-16.0,-88.0,24.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.637}]},{"id":"SAwck8h3LCeQ","coordinates":[18.0,-88.0,-8.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":3.838}]},{"id":"VP8tEE9BRkNb","coordinates":[-16.0,-90.0,24.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.467}]},{"id":"ZkGzXZxd9YHC","coordinates":[16.0,88.0,-8.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":3.458}]},{"id":"npqjSXG3hpEL","coordinates":[18.0,6.0,20.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.322}]},{"id":"yLXpgPNo6kcA","coordinates":[28.0,-68.0,-38.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":3.718}]}],"images":[]},{"id":"xYB5yzrEtJvV","user":"auth0|66546ba3a94038ae9812fce4","name":"Lenient ","metadata":null,"description":"Decreases","conditions":[],"weights":[],"points":[{"id":"9JzweTnfTvfV","coordinates":[42.0,-68.0,-22.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.931}]},{"id":"QumVEdBuTLWa","coordinates":[-6.0,-86.0,-14.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.381}]}],"images":[]}]},{"id":"tKb6rLkin7m2","created_at":"2025-06-15T13:46:19.009398+00:00","updated_at":"2025-07-31T08:36:23.388371+00:00","user":"auth0|66546ba3a94038ae9812fce4","name":"Convergent and divergent gray matter volume abnormalities in unaffected first-degree relatives and ultra-high risk individuals of schizophrenia.","description":"\nHigh-risk populations of schizophrenia can be mainly identified as genetic high-risk based on putative endophenotypes or ultra-high-risk (UHR) based on clinically manifested symptoms. Previous studies have consistently shown brain structural abnormalities in both genetic high-risk and UHR individuals. In this study, we aimed to disentangle the convergent and divergent pattern of gray matter alterations between UHR and unaffected first-degree relatives from genetic high-risk individuals. We used structural MRI scans and voxel-based morphometry method to examine gray matter volume (GMV) differences among 23 UHR subjects meeting the Structured Interview for Prodromal Syndromes (SIPS) criteria, 18 unaffected first-degree relatives (UFDR), 26 first-episode schizophrenia patients (FES) and 54 healthy controls (CN). We found that a number of brain regions exhibited a monotonically decreasing trend of GMV from CN to UFDR to UHR to FES. Compared with CN, the UHR subjects showed significant decreases of GMV similar to the patients in the inferior temporal gyrus, fusiform gyrus, middle occipital gyrus, insula, and limbic regions. Moreover, the UHR transformed subgroup had significantly lower GMV than UHR non-transformed subgroup in the right inferior temporal/fusiform gyrus. On the other hand, the UFDR subjects only showed significant GMV decreases in the inferior temporal gyrus and fusiform. Moreover, we found GMV in the occipital lobe was negatively correlated with the UHR subjects' composite positive symptom of SIPS, and GMV in the cerebellum was positively correlated with FES subjects' symptom severity. Our results suggest that GMV deficits and regional dysfunction are evident prior to the onset of psychosis and are more prominent in the UHR than the UFDR individuals.","publication":"Schizophrenia (Heidelberg, Germany)","doi":"10.1038/s41537-022-00261-9","pmid":"35853913","authors":"Bei Lin, Xian-Bin Li, Sen Ruan, Yu-Xin Wu, Chao-Yue Zhang, Chuan-Yue Wang, Lu-Bin Wang","year":2022,"metadata":{"sample_size":59,"sample_sizes":null},"source":"neurostore","source_id":"U7c7XqLLsWB7","source_updated_at":"2025-06-05T06:56:03.640436+00:00","analyses":[{"id":"6zjG5AEHAnnE","user":"auth0|66546ba3a94038ae9812fce4","name":"Strict","metadata":null,"description":"Decreases","conditions":[],"weights":[],"points":[{"id":"JpnUSjuE6ois","coordinates":[-33.0,-10.5,-48.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":2.96}]}],"images":[]}]},{"id":"A9xmqSj6uTGc","created_at":"2025-06-15T13:47:48.635722+00:00","updated_at":"2025-07-31T08:36:35.572992+00:00","user":"auth0|66546ba3a94038ae9812fce4","name":"Convergent evidence from multimodal imaging reveals amygdala abnormalities in schizophrenic patients and their first-degree relatives.","description":"BACKGROUND: Shared neuropathological features between schizophrenic patients and their first-degree relatives have potential as indicators of genetic vulnerability to schizophrenia. We sought to explore genetic influences on brain morphology and function in schizophrenic patients and their relatives. METHODS: Using a multimodal imaging strategy, we studied 33 schizophrenic patients, 55 of their unaffected parents, 30 healthy controls for patients, and 29 healthy controls for parents with voxel-based morphometry of structural MRI scans and functional connectivity analysis of resting-state functional MRI data. RESULTS: Schizophrenic patients showed widespread gray matter reductions in the bilateral frontal cortices, bilateral insulae, bilateral occipital cortices, left amygdala and right thalamus, whereas their parents showed more localized reductions in the left amygdala, left thalamus and right orbitofrontal cortex. Patients and their parents shared gray matter loss in the left amygdala. Further investigation of the resting-state functional connectivity of the amygdala in the patients showed abnormal functional connectivity with the bilateral orbitofrontal cortices, bilateral precunei, bilateral dorsolateral frontal cortices and right insula. Their parents showed slightly less, but similar changes in the pattern in the amygdala connectivity. Co-occurrences of abnormal connectivity of the left amygdala with the left orbitofrontal cortex, right dorsolateral frontal cortex and right precuneus were observed in schizophrenic patients and their parents. CONCLUSIONS: Our findings suggest a potential genetic influence on structural and functional abnormalities of the amygdala in schizophrenia. Such information could help future efforts to identify the endophenotypes that characterize the complex disorder of schizophrenia.","publication":"PloS one","doi":"10.1371/journal.pone.0028794","pmid":"22174900","authors":"Tian L, Meng C, Yan H, Zhao Q, Liu Q, Yan J, Han Y, Yuan H, Wang L, Yue W, Zhang Y, Li X, Zhu C, He Y, Zhang D","year":2011,"metadata":{"sample_size":84,"sample_sizes":null},"source":"neurostore","source_id":"7eJF6jsTQ2K9","source_updated_at":"2024-03-21T20:00:49.303112+00:00","analyses":[{"id":"a49ayyy4QbfQ","user":"auth0|66546ba3a94038ae9812fce4","name":"Lenient","metadata":null,"description":"Decreases","conditions":[],"weights":[],"points":[{"id":"KYXfqqzmKfAi","coordinates":[-22.0,-5.0,-2.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":3.64}]},{"id":"PFNAnLq3vWra","coordinates":[-35.0,-1.0,8.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.05}]},{"id":"SCoiDrm96kSU","coordinates":[20.0,52.0,-10.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.43}]},{"id":"X28xwxFJkMgo","coordinates":[-26.0,4.0,-14.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.52}]},{"id":"aNehRXmxbPYp","coordinates":[-21.0,-20.0,10.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":3.46}]},{"id":"aQrL2fxjS9tf","coordinates":[21.0,57.0,2.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":3.01}]},{"id":"apZTo9znBrRk","coordinates":[-16.0,-7.0,6.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":3.58}]},{"id":"ePLZiVkeJ4Ds","coordinates":[8.0,60.0,-13.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":3.86}]},{"id":"gtTDUwG4LxKX","coordinates":[-13.0,12.0,-13.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":2.93}]}],"images":[]}]},{"id":"XoCyYUbWPN6D","created_at":"2025-06-15T13:54:32.219417+00:00","updated_at":"2025-06-18T17:11:41.717363+00:00","user":"auth0|66546ba3a94038ae9812fce4","name":"Decreased gray matter volume in the left middle temporal gyrus as a candidate biomarker for schizophrenia: A study of drug naive, first-episode schizophrenia patients and unaffected siblings","description":"BACKGROUND: Studies have shown that patients with schizophrenia and their siblings share decreased gray matter (GM) volumes in certain brain regions, which may represent candidate endophenotypes of schizophrenia. However, the specificity and utility of these possible endophenotypes in relation to schizophrenia remain unclear. METHODS: Twenty drug-naive, first-episode schizophrenia patients and 20 first-degree unaffected siblings from the same families as the patients (USS group), a separate group of 25 first-degree unaffected siblings of schizophrenia patients from other families (USO group), and 43 healthy controls were recruited. Voxel-based morphometry (VBM) was used to analyze structural imaging data. RESULTS: The VBM analysis showed a significant difference in GM volume between the combined sibling group and the control group in the left middle temporal gyrus (MTG). Group comparison showed that the patients, the USS, and the USO had significantly decreased GM volume of the left MTG compared with the controls; such a difference did not exist among the patients and the two sibling groups. A receiver operating characteristic curve (ROC curve) analysis showed good predictive value of the mean cluster volume in the left MTG to distinguish patients, USS, and USO from healthy controls. There were no significant correlations between the mean cluster volume in the left MTG and clinical variables in the patients. CONCLUSIONS: The GM volume decrease of the left MTG may be utilized as a candidate biomarker for schizophrenia. The novel design of including a USO group in our study enhances both the specificity and the heritability of the biomarker identified.","publication":"Schizophrenia research","doi":"10.1016/j.schres.2014.07.051","pmid":"25156295","authors":"Guo, Wenbin;Hu, Maorong;Fan, Xiaoduo;Liu, Feng;Wu, Renrong;Chen, Jindong;Guo, Xiaofeng;Xiao, Changqing;Quan, Meina;Chen, Huafu;Zhai, Jinguo;Zhao, Jingping","year":2014,"metadata":{"sample_size":63},"source":"neurostore","source_id":"53EpYTdTCAcL","source_updated_at":"2024-03-22T17:58:43.668136+00:00","analyses":[]},{"id":"D7HcUiZRZAPU","created_at":"2025-06-15T13:57:09.361267+00:00","updated_at":"2025-07-31T08:36:48.143697+00:00","user":"auth0|66546ba3a94038ae9812fce4","name":"Decreased left middle temporal gyrus volume in antipsychotic drug-naive, first-episode schizophrenia patients and their healthy unaffected siblings.","description":"The shared neuropathological characteristics of patients with schizophrenia and their siblings might represent intermediate phenotypes that could be used to investigate genetic susceptibility to the illness. We sought to discover gray matter volume differences in patients with schizophrenia and their unaffected siblings with voxel-based morphometry (VBM). | We recruited antipsychotic drug-naive, first-episode schizophrenia (FES) patients, their unaffected siblings and age-, sex- and handedness-matched healthy controls. We used VBM to investigate differences in gray matter volume among the 3 groups. | There were significant gray matter volumetric differences among the 3 groups in bilateral hippocampal and parahippocampal gyri, bilateral middle temporal gyri, and superior temporal gyri (FDR p<0.05). Patients had significant regional gray matter reduction in all regions listed above compared with healthy volunteers, and their gray matter volume in the right hippocampus and parahippocampus was also lower than the sibling group. The sibling group had significantly lower volumes compared to healthy individuals only in the left middle temporal gyrus, and volume of this region was not different between siblings and patients. | Our findings confirm and extend previous VBM analyses in schizophrenia and it indicate that schizophrenia may be characterized by an abnormal development of cerebral lateralization. Furthermore, these data argue that patients and their unaffected siblings might share decreases in the gray matter volume of the left middle temporal gyrus, and this regional reduction might be a potential endophenotype for schizophrenia.","publication":"Schizophrenia research","doi":"10.1016/j.schres.2012.12.018","pmid":"23360727","authors":"Hu M, Li J, Eyler L, Guo X, Wei Q, Tang J, Liu F, He Z, Li L, Jin H, Liu Z, Wang J, Liu F, Chen H, Zhao J","year":2013,"metadata":{"sample_size":104,"sample_sizes":null},"source":"neurostore","source_id":"6BRiehY4wT2v","source_updated_at":"2023-07-17T03:27:48.613627+00:00","analyses":[{"id":"mYSjGwyg4dNh","user":"auth0|66546ba3a94038ae9812fce4","name":"Strict","metadata":null,"description":"Decreases","conditions":[],"weights":[],"points":[{"id":"XSygDD8uFK4Z","coordinates":[-60.0,-7.5,-10.5],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.745}]}],"images":[]}]},{"id":"yqRqpGDByRpk","created_at":"2025-06-15T13:59:04.854030+00:00","updated_at":"2025-07-31T08:36:59.187510+00:00","user":"auth0|66546ba3a94038ae9812fce4","name":"Differing brain structural correlates of familial and environmental risk for major depressive disorder revealed by a combined VBM/pattern recognition approach.","description":"BACKGROUND\nNeuroimaging traits of either familial or environmental risk for major depressive disorder (MDD) have been interpreted as possibly useful vulnerability markers. However, the simultaneous occurrence of familial and environmental risk might prove to be a major obstacle in the attempt of recent studies to confine the precise impact of each of these conditions on brain structure. Moreover, the exclusive use of group-level analyses does not permit prediction of individual illness risk which would be the basic requirement for the clinical application of imaging vulnerability markers. Hence, we aimed to distinguish between brain structural characteristics of familial predisposition and environmental stress by using both group- and individual-level analyses.\nMETHOD\nWe investigated grey matter alterations between 20 healthy control subjects (HC) and 20 MDD patients; 16 healthy first-degree relatives of MDD patients (FH+) and 20 healthy subjects exposed to former childhood maltreatment (CM+) by using a combined VBM/pattern recognition approach.\nRESULTS\nWe found similar grey matter reductions in the insula and the orbitofrontal cortex in patients and FH+ subjects and in the hippocampus in patients and CM+ subjects. No direct overlap in grey matter alterations was found between FH+ and CM+ subjects. Pattern classification successfully detected subjects at risk for the disease even by strictly focusing on morphological traits of MDD.\nCONCLUSIONS\nFamilial and environmental risk factors for MDD are associated with differing morphometric anomalies. Pattern recognition might be a promising instrument in the search for and future application of vulnerability markers for MDD.\n","publication":"Psychological medicine","doi":"10.1017/S0033291715001683","pmid":"26355299","authors":"N Opel, P Zwanzger, R Redlich, D Grotegerd, K Dohm, V Arolt, W Heindel, H Kugel, U Dannlowski","year":2016,"metadata":{"sample_size":36,"sample_sizes":null},"source":"neurostore","source_id":"HMtdj4hStmdA","source_updated_at":"2025-06-05T06:56:03.640436+00:00","analyses":[{"id":"3voGQpNdiSQ9","user":"auth0|66546ba3a94038ae9812fce4","name":"Strict","metadata":null,"description":"Decreases","conditions":[],"weights":[],"points":[{"id":"FR8RFrquhP3R","coordinates":[-6.0,30.0,16.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.33}]},{"id":"HztMfJvfieYM","coordinates":[33.0,21.0,-5.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.42}]},{"id":"Z3uW9T6M6E4o","coordinates":[-62.0,-55.0,25.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.9}]},{"id":"pk9oRRp98nBY","coordinates":[-32.0,9.0,6.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":6.1}]},{"id":"pvBcJzdkx3CG","coordinates":[21.0,-48.0,-2.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.45}]}],"images":[]}]},{"id":"oYTBHYBZeUhD","created_at":"2025-06-15T14:00:25.927258+00:00","updated_at":"2025-07-31T08:37:12.519955+00:00","user":"auth0|66546ba3a94038ae9812fce4","name":"Dissociation of functional and anatomical brain abnormalities in unaffected siblings of schizophrenia patients.","description":"Schizophrenia patients and their unaffected siblings share similar brain functional and structural abnormalities. However, no study is engaged to investigate whether and how functional abnormalities are related to structural abnormalities in unaffected siblings. This study was undertaken to examine the association between functional and anatomical abnormalities in unaffected siblings. | Forty-six unaffected siblings of schizophrenia patients and 46 age-, sex-, and education-matched healthy controls underwent structural and resting-state functional magnetic resonance imaging scanning. Voxel-based morphometry (VBM), amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) were utilized to analyze imaging data. | The VBM analysis showed gray matter volume decreases in the fronto-temporal regions (the left middle temporal gyrus and right inferior frontal gyrus, orbital part) and increases in basal ganglia system (the left putamen). Functional abnormalities measured by ALFF and fALFF mainly involved in the fronto-limbic-sensorimotor circuit (decreased ALFF in bilateral middle frontal gyrus and the right middle cingulate gyrus, and decreased fALFF in the right inferior frontal gyrus, orbital part; and increased ALFF in the left fusiform gyrus and left lingual gyrus, and increased fALFF in bilateral calcarine cortex). No significant correlation was found between functional and anatomical abnormalities in the sibling group. | A dissociation pattern of brain regions with functional and anatomical abnormalities is observed in unaffected siblings. | Our findings suggest that brain functional and anatomical abnormalities might be present independently in unaffected siblings of schizophrenia patients.","publication":"Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology","doi":"10.1016/j.clinph.2014.08.016","pmid":"25240248","authors":"Guo W, Song Y, Liu F, Zhang Z, Zhang J, Yu M, Liu J, Xiao C, Liu G, Zhao J","year":2015,"metadata":{"sample_size":92,"sample_sizes":null},"source":"neurostore","source_id":"5XESNQfnczzd","source_updated_at":"2023-07-17T03:27:48.613627+00:00","analyses":[{"id":"6XFLEtPSmP53","user":"auth0|66546ba3a94038ae9812fce4","name":"Strict","metadata":null,"description":"Decreases","conditions":[],"weights":[],"points":[{"id":"3Ew56Usvg48d","coordinates":[39.0,21.0,-19.5],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":-4.4794}]},{"id":"KtXLjVub8eTV","coordinates":[-57.0,6.0,-24.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":-3.9192}]}],"images":[]},{"id":"EGXTAGVWRrGS","user":"auth0|66546ba3a94038ae9812fce4","name":"Strict ","metadata":null,"description":"Increases","conditions":[],"weights":[],"points":[{"id":"ogRyoJoyjSex","coordinates":[-30.0,-12.0,3.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":3.6271}]}],"images":[]}]},{"id":"FWTPDpntgAxB","created_at":"2025-06-15T14:12:48.769156+00:00","updated_at":null,"user":"auth0|66546ba3a94038ae9812fce4","name":"Grey matter, an endophenotype for schizophrenia? A voxel-based morphometry study in siblings of patients with schizophrenia.","description":"BACKGROUND\nGrey matter, both volume and concentration, has been proposed as an endophenotype for schizophrenia given a number of reports of grey matter abnormalities in relatives of patients with schizophrenia. However, previous studies on grey matter abnormalities in relatives have produced inconsistent results. The aim of the present study was to examine grey matter differences between controls and siblings of patients with schizophrenia and to examine whether the age, genetic loading or subclinical psychotic symptoms of selected individuals could explain the previously reported inconsistencies.\nMETHODS\nWe compared the grey matter volume and grey matter concentration of healthy siblings of patients with schizophrenia and healthy controls matched for age, sex and education using voxel-based morphometry (VBM). Furthermore, we selected subsamples based on age (< 30 yr), genetic loading and subclinical psychotic symptoms to examine whether this would lead to different results.\nRESULTS\nWe included 89 siblings and 69 controls in our study. The results showed that siblings and controls did not differ significantly on grey matter volume or concentration. Furthermore, specifically selecting participants based on age, genetic loading or subclinical psychotic symptoms did not alter these findings.\nLIMITATIONS\nThe main limitation was that subdividing the sample resulted in smaller samples for the subanalyses. Furthermore, we used MRI data from 2 different scanner sites.\nCONCLUSION\nThese results indicate that grey matter measured through VBM might not be a suitable endophenotype for schizophrenia.\n","publication":"Journal of psychiatry & neuroscience : JPN","doi":"10.1503/jpn.140064","pmid":"25768029","authors":"Jorien van der Velde, Paula M Gromann, Marte Swart, Lieuwe de Haan, Durk Wiersma, Richard Bruggeman, Lydia Krabbendam, André Aleman","year":2015,"metadata":{"sample_size":null},"source":"neurostore","source_id":"spvYoXsZty4S","source_updated_at":"2025-06-05T06:56:03.640436+00:00","analyses":[]},{"id":"zxuvY4i5Ac3J","created_at":"2025-06-15T14:16:59.145577+00:00","updated_at":"2025-07-31T08:37:36.933952+00:00","user":"auth0|66546ba3a94038ae9812fce4","name":"Larger amygdala volume in first-degree relatives of patients with major depression.","description":"OBJECTIVE\nAlthough a heritable contribution to risk for major depressive disorder (MDD) has been established and neural alterations in patients have been identified through neuroimaging, it is unclear which brain abnormalities are related to genetic risk. Studies on brain structure of high-risk subjects - such as individuals carrying a familial liability for the development of MDD - can provide information on the potential usefulness of these measures as intermediate phenotypes of MDD.\nMETHODS\n63 healthy first-degree relatives of patients with MDD and 63 healthy controls underwent structural magnetic resonance imaging. Regional gray matter volumes were analyzed via voxel-based morphometry (VBM).\nRESULTS\nWhole-brain analysis revealed significantly larger gray matter volume in the bilateral amygdala in first-degree relatives of patients with MDD. Furthermore, relatives showed significantly larger gray matter volume in anatomical structures found relevant to MDD in previous literature, specifically in the bilateral hippocampus and amygdala as well as the left dorsolateral prefrontal cortex (DLPFC). Bilateral DLPFC volume correlated positively with the experience of negative affect.\nCONCLUSIONS\nLarger gray matter volume in healthy relatives of MDD patients point to a possible vulnerability mechanism in MDD etiology and therefore extend knowledge in the field of high-risk approaches in MDD.\n","publication":"NeuroImage. Clinical","doi":"10.1016/j.nicl.2014.05.015","pmid":"25003028","authors":"Nina Romanczuk-Seiferth, Lydia Pöhland, Sebastian Mohnke, Maria Garbusow, Susanne Erk, Leila Haddad, Oliver Grimm, Heike Tost, Andreas Meyer-Lindenberg, Henrik Walter, Torsten Wüstenberg, Andreas Heinz","year":2014,"metadata":{"sample_size":126,"sample_sizes":null},"source":"neurostore","source_id":"QzDUzvUirSen","source_updated_at":"2025-06-05T06:56:03.640436+00:00","analyses":[{"id":"CZbXnyNmZB2A","user":"auth0|66546ba3a94038ae9812fce4","name":"Strict","metadata":null,"description":"Increases","conditions":[],"weights":[],"points":[{"id":"djrSr2FhGFiX","coordinates":[-21.0,2.0,-14.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"Z","value":5.67}]},{"id":"nwsBZ8ouJCxi","coordinates":[26.0,0.0,-15.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"Z","value":5.42}]}],"images":[]}]},{"id":"MoCtKmNnWpgW","created_at":"2025-06-15T14:18:59.762263+00:00","updated_at":"2025-07-31T08:37:50.297453+00:00","user":"auth0|66546ba3a94038ae9812fce4","name":"Neuroanatomical correlates of genetic risk for bipolar disorder: A voxel-based morphometry study in bipolar type I patients and healthy first degree relatives.","description":"BACKGROUND: Bipolar disorder (BD) is a highly heritable mental illness which is associated with neuroanatomical abnormalities. Investigating healthy individuals at high genetic risk for bipolar disorder may help to identify neuroanatomical markers of risk and resilience without the confounding effects of burden of illness or medication., METHODS: Structural magnetic resonance imaging scans were acquired from 30 euthymic patients with BD-I (BP), 28 healthy first degree relatives of BD-I patients (HR), and 30 healthy controls (HC). Data was analyzed using DARTEL for voxel based morphometry in SPM8., RESULTS: Whole-brain analysis revealed a significant main effect of group in the gray matter volume in bilateral inferior frontal gyrus, left parahippocampal gyrus, left lingual gyrus and cerebellum, posterior cingulate gyrus, and supramarginal gyrus (alphasim corrected (<=0.05 FWE)). Post-hoc t-tests showed that inferior frontal gyrus volumes were bilaterally larger both in BP and HR than in HC. BP and HR also had smaller cerebellar volume compared with HC. In addition, BP had smaller left lingual gyrus volume, whereas HR had larger left parahippocampal and supramarginal gyrus volume compared with HC., LIMITATIONS: This study was cross-sectional and the sample size was not large. All bipolar patients were on medication, therefore we were not able to exclude medication effects in bipolar group in this study., CONCLUSIONS: Our findings suggest that increased inferior frontal gyrus and decreased cerebellar volumes might be associated with genetic predisposition for bipolar disorder. Longitudinal studies are needed to better understand the predictive and prognostic value of structural changes in these regions. Copyright © 2015 Elsevier B.V. All rights reserved.","publication":"Journal of affective disorders","doi":"10.1016/j.jad.2015.06.055","pmid":null,"authors":"Aybala Saricicek, Nefize Yalin, Ceren Hidiroglu, Berrin Cavusoglu, Cumhur Tas, Deniz Ceylan, Nabi Zorlu, Emel Ada, Zeliha Tunca, Aysegul Ozerdem","year":2015,"metadata":{"sample_size":54,"sample_sizes":null},"source":"neurostore","source_id":"yRX4gSBvqVjN","source_updated_at":"2024-07-30T00:09:32.400584+00:00","analyses":[{"id":"E45Xu8dmww2x","user":"auth0|66546ba3a94038ae9812fce4","name":"Strict ","metadata":null,"description":"Decreases","conditions":[],"weights":[],"points":[{"id":"TF3pwxQpe5oL","coordinates":[5.0,-67.0,-5.0],"kind":null,"space":"TAL","image":null,"label_id":null,"values":[{"kind":"T","value":3.8}]}],"images":[]},{"id":"YqoNKCaKdV6K","user":"auth0|66546ba3a94038ae9812fce4","name":"Strict","metadata":null,"description":"Increases","conditions":[],"weights":[],"points":[{"id":"E3wAFRjreZYT","coordinates":[-55.0,-49.0,21.0],"kind":null,"space":"TAL","image":null,"label_id":null,"values":[{"kind":"T","value":3.7}]},{"id":"WsD2fJaoqfBK","coordinates":[-20.0,17.0,-14.0],"kind":null,"space":"TAL","image":null,"label_id":null,"values":[{"kind":"T","value":3.7}]},{"id":"XuR2QiZBgE7u","coordinates":[14.0,16.0,-19.0],"kind":null,"space":"TAL","image":null,"label_id":null,"values":[{"kind":"T","value":3.4}]},{"id":"wmPpCHkKizq4","coordinates":[-38.0,-30.0,-16.0],"kind":null,"space":"TAL","image":null,"label_id":null,"values":[{"kind":"T","value":3.9}]}],"images":[]}]},{"id":"ghD9cKZU3W5G","created_at":"2025-06-15T14:24:35.678183+00:00","updated_at":"2025-07-31T08:38:11.226509+00:00","user":"auth0|66546ba3a94038ae9812fce4","name":"Shared Transdiagnostic Neuroanatomical Signatures Across First-episode Patients with Major Psychiatric Diseases and Individuals at Familial Risk.","description":"BACKGROUND\nNowadays, increasing evidence has found transdiagnostic neuroimaging biomarkers across major psychiatric disorders (MPDs). However, it remains to be known whether this transdiagnostic pattern of abnormalities could also be seen in individuals at familial high-risk for MPDs (FHR). We aimed to examine shared neuroanatomical endophenotypes and protective biomarkers for MPDs.\nMETHODS\nThis study examined brain grey matter volume (GMV) of individuals by voxel-based morphometry method. A total of 287 individuals were included, involving 100 first-episode medication-naive MPDs, 87 FHR, and 110 healthy controls (HC). They all underwent high-resolution structural magnetic resonance imaging (MRI).\nRESULTS\nAt the group level, we found MPDs were characterized by decreased GMV in the right fusiform gyrus, the right inferior occipital gyrus, and the left anterior and middle cingulate gyri compared to HC and FHR. Of note, the GMV of the left superior temporal gyrus was increased in FHR relative to MPDs and HC. At the subgroup level, the comparisons within the FHR group did not return any significant difference, and we found GMV difference among subgroups within the MPDs group only in the opercular part of the right inferior frontal gyrus.\nCONCLUSION\nTogether, our findings uncover common structural disturbances across MPDs and substantial changes in grey matter that may relate to high hereditary risk across FHR, potentially underscoring the importance of a transdiagnostic way to explore the neurobiological mechanisms of major psychiatric disorders.\n","publication":"NeuroImage. Clinical","doi":"10.1016/j.nicl.2022.103074","pmid":"35691252","authors":"Linna Jia, Xiaowei Jiang, Qikun Sun, Jian Zhou, Linzi Liu, Ting Sun, Pengshuo Wang, Yanqing Tang","year":2022,"metadata":{"sample_size":187,"sample_sizes":null},"source":"neurostore","source_id":"WkUmHSjJdfou","source_updated_at":"2025-06-05T06:56:03.640436+00:00","analyses":[{"id":"Yj6PTzuXW76v","user":"auth0|66546ba3a94038ae9812fce4","name":"Strict","metadata":null,"description":"Increases","conditions":[],"weights":[],"points":[{"id":"79VuNLJFp9vK","coordinates":[-55.5,-34.5,15.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"Z","value":-4.034}]}],"images":[]}]},{"id":"np2TjKL92Exq","created_at":"2025-07-29T12:39:28.690435+00:00","updated_at":null,"user":"auth0|66546ba3a94038ae9812fce4","name":"Voxel-based morphometry of patients with schizophrenia or bipolar disorder and their unaffected relatives.","description":"Background: Structural brain abnormalities in schizophrenia are well replicated; many emerge before the onset of illness and are present in relatives who remain well. Structural changes in bipolar disorder are less clearly established. The possibility that structural abnormalities might provide a means by which the disorders might be separated is one that has attracted limited research effort. This study sought to examine these issues and clarify the associations of phenotypic expression and genetic liability. Methods: Forty-nine control subjects, 71 patients, and 72 unaffected relatives were recruited for the study. Patients included those with schizophrenia from families affected by schizophrenia alone, those with bipolar disorder from families affected by bipolar disorder alone, and those with bipolar disorder from families affected by both bipolar disorder and schizophrenia. Unaffected relatives were recruited from the families of the three patient groups. Subjects underwent a mag","publication":"Biological Psychiatry","doi":null,"pmid":null,"authors":"Andrew M. McIntosh, Dominic E. Job, T. William J. Moorhead, Lesley K. Harrison, Karen Forrester, Stephen M. Lawrie, Eve C. Johnstone","year":2004,"metadata":null,"source":null,"source_id":null,"source_updated_at":null,"analyses":[]},{"id":"aXGtsUrGU22S","created_at":"2025-07-29T12:39:28.690435+00:00","updated_at":"2025-08-05T14:16:58.376362+00:00","user":"auth0|66546ba3a94038ae9812fce4","name":"Family history of alcohol use disorder is associated with brain structural and functional changes in healthy first-degree relatives.","description":"Background: Neuroimaging studies of vulnerability to Alcohol Use Disorder (AUD) have identified structural and functional variations which might reflect inheritable features in alcohol-naïve relatives of AUD individuals (FH+) compared to controls having no such family history (FH-). However, prior research did not simultaneously account for childhood maltreatment, any clinically significant disorder and maternal AUD. Therefore, we mainly aimed to investigate the brain structure and reward-related neural activations (fMRI), using whole-brain analysis in FH+ young adults with no prevalent confounders. Methods: 46 FH+ and 45 FH- male and female participants had no severe childhood maltreatment exposure, neither any psychiatric disorder or AUD, nor a prenatal exposure to maternal AUD. We used a 3 T MRI coupled with a whole brain voxel-based method to compare between groups the grey matter volumes and activations in response to big versus small wins during a Monetary Incentive Delay task. ","publication":"European Psychiatry","doi":null,"pmid":null,"authors":"Irina Filippi, Nicolas Hoertel, Eric Artiges, Guillaume Airagnes, Christophe Guérin-Langlois, Anne-Sophie Seigneurie, Pauline Frère, Manon Dubol, François Guillon, Hervé Lemaître, Mehdi Rahim, Jean-Luc Martinot, Frédéric Limosin","year":2019,"metadata":{"sample_size":73},"source":null,"source_id":null,"source_updated_at":null,"analyses":[{"id":"uvP9q7tGys4r","user":"auth0|66546ba3a94038ae9812fce4","name":"Lenient","metadata":null,"description":"Decreases ","conditions":[],"weights":[],"points":[{"id":"cKtsoSRp5kRQ","coordinates":[28.0,40.0,34.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":5.97}]},{"id":"AmA2PdKFBUoH","coordinates":[44.0,48.0,-9.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.8}]},{"id":"8u9vH3APNH7y","coordinates":[46.0,44.0,0.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":3.89}]},{"id":"eFUxivzpbepb","coordinates":[48.0,10.0,2.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":5.79}]},{"id":"ucRv2vpgacgo","coordinates":[56.0,22.0,14.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.36}]},{"id":"crWnuFoKWczs","coordinates":[-16.0,10.0,-20.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.23}]},{"id":"vvmkCuVWWvPE","coordinates":[18.0,10.0,-18.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":5.04}]},{"id":"Jqo7fWayiH2B","coordinates":[-6.0,20.0,-10.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.59}]},{"id":"VJK2DsgtQKSd","coordinates":[40.0,-4.0,8.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.82}]},{"id":"ZxvthSyd5Wrz","coordinates":[-6.0,33.0,-16.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.18}]},{"id":"dt3ks2xq7Zk2","coordinates":[4.0,44.0,-21.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.15}]},{"id":"usXdZtmnoA8z","coordinates":[8.0,10.0,-10.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.51}]},{"id":"p62Pcp2Enypg","coordinates":[-6.0,12.0,-8.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.17}]},{"id":"YPxU97Skup5z","coordinates":[-2.0,-44.0,33.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.92}]},{"id":"oXU7LdH65Y9p","coordinates":[4.0,-54.0,21.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.62}]},{"id":"6MHLnBc2wgpe","coordinates":[-4.0,-57.0,32.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.21}]},{"id":"98E3bDFJSrj6","coordinates":[10.0,-60.0,10.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":3.69}]},{"id":"6PfQp6rsiFF5","coordinates":[44.0,-15.0,44.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.47}]},{"id":"GDvBdXyeNBLg","coordinates":[39.0,-3.0,54.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":4.17}]},{"id":"WFeL6MPS9atc","coordinates":[54.0,-8.0,36.0],"kind":null,"space":"MNI","image":null,"label_id":null,"values":[{"kind":"T","value":3.36}]}],"images":[]}]},{"id":"2pzVXAREyHEt","created_at":"2025-07-29T12:41:47.009912+00:00","updated_at":"2025-07-31T08:38:01.150781+00:00","user":"auth0|66546ba3a94038ae9812fce4","name":"Neuroanatomical differences between familial and sporadic schizophrenia and their parents: An optimized voxel-based morphometry study","description":null,"publication":null,"doi":"https://doi.org/10.1016/j.pscychresns.2008.02.004","pmid":"19168334","authors":"Su Lui","year":2009,"metadata":{"sample_size":20,"sample_sizes":null},"source":"neurostore","source_id":"46suJAejc6Pr","source_updated_at":"2023-06-23T03:08:47.052372+00:00","analyses":[{"id":"qsWEsAqVbE5w","user":"auth0|66546ba3a94038ae9812fce4","name":"Strict","metadata":null,"description":"Decreases","conditions":[],"weights":[],"points":[{"id":"izZqfU54LWxU","coordinates":[-4.0,51.0,-24.0],"kind":null,"space":"TAL","image":null,"label_id":null,"values":[{"kind":"T","value":4.0}]},{"id":"ApusMwQsEayy","coordinates":[17.0,29.0,-22.0],"kind":null,"space":"TAL","image":null,"label_id":null,"values":[{"kind":"T","value":4.1}]},{"id":"4vYEKdaiEs82","coordinates":[-23.0,26.0,-21.0],"kind":null,"space":"TAL","image":null,"label_id":null,"values":[{"kind":"T","value":3.8}]},{"id":"88eDakvuEjWU","coordinates":[-28.0,-22.0,-17.0],"kind":null,"space":"TAL","image":null,"label_id":null,"values":[{"kind":"T","value":3.4}]},{"id":"VnD4mrvqKuDY","coordinates":[40.0,-11.0,7.0],"kind":null,"space":"TAL","image":null,"label_id":null,"values":[{"kind":"T","value":4.9}]},{"id":"WAhkNd32FcWV","coordinates":[-24.0,-69.0,-28.0],"kind":null,"space":"TAL","image":null,"label_id":null,"values":[{"kind":"T","value":4.4}]},{"id":"bB5o7whKrFRW","coordinates":[16.0,-59.0,-21.0],"kind":null,"space":"TAL","image":null,"label_id":null,"values":[{"kind":"T","value":4.4}]},{"id":"oo7Nr5wJiear","coordinates":[-43.0,-14.0,15.0],"kind":null,"space":"TAL","image":null,"label_id":null,"values":[{"kind":"T","value":3.6}]},{"id":"NsVemAXc88vM","coordinates":[41.0,-90.0,4.0],"kind":null,"space":"TAL","image":null,"label_id":null,"values":[{"kind":"T","value":4.8}]}],"images":[]}]}]}
diff --git a/nimare/tests/test_io.py b/nimare/tests/test_io.py
index 65c29cf..876bec0 100644
--- a/nimare/tests/test_io.py
+++ b/nimare/tests/test_io.py
@@ -38,9 +38,7 @@ def test_convert_nimads_to_dataset_sample_sizes(
     assert "sample_sizes" in dset.metadata.columns
 
 
-def test_convert_nimads_to_dataset_single_sample_size(
-    example_nimads_studyset, example_nimads_annotation
-):
+def test_convert_nimads_to_dataset_single_sample_size(example_nimads_studyset):
     """Test conversion of nimads JSON to nimare dataset with a single sample size value."""
     studyset = Studyset(example_nimads_studyset)
     for study in studyset.studies:
@@ -53,6 +51,16 @@ def test_convert_nimads_to_dataset_single_sample_size(
     assert "sample_sizes" in dset.metadata.columns
 
 
+def test_convert_nimads_to_dataset_wonky_sample_size(sample_size_nimads_studyset):
+    """Test conversion of nimads JSON to nimare dataset with wonky sample size values."""
+    studyset = Studyset(sample_size_nimads_studyset)
+
+    dset = io.convert_nimads_to_dataset(studyset)
+
+    assert isinstance(dset, nimare.dataset.Dataset)
+    assert "sample_sizes" in dset.metadata.columns
+
+
 @pytest.mark.parametrize(
     "sample_sizes_val,sample_size_val,expect_col,expect_warning",
     [