The brain structure of patients with recent-onset psychosis and depression can offer important biological insights into these illnesses and how they might develop.
In a new study published in Biological Psychiatry, researchers at the University of Birmingham show that it’s possible to identify patients most susceptible to poor outcomes by examining structural MRI scans of the brain.
By identifying these patients in the early stages of their illness, clinicians will be able to offer more targeted and effective treatments.
‘Currently, we diagnose most mental health disorders based on a patient’s history, symptoms, and clinical observations, rather than on biological information,’ says lead author Paris Alexandros Lalousis. ‘That means patients might have similar underlying biological mechanisms in their illness but different diagnoses. By understanding those mechanisms more fully, we can give clinicians better tools to use in planning treatments.’
The researchers used data from around 300 patients with recent-onset psychosis and recent-onset depression in the PRONIA study. PRONIA is a European Union-funded cohort study investigating prognostic tools for psychoses which is taking place across seven European research centres, including Birmingham.
The researchers used a machine-learning algorithm to assess data from patients’ brain scans and sort these into groups or clusters. Two clusters were identified based on the scans, each of which contained both patients with psychosis and patients with depression. Each cluster revealed distinctive characteristics which related strongly to their likelihood of recovery.
In the first cluster, lower volumes of grey matter – the darker tissue inside the brain involved in muscle control and functions such as memory, emotions, and decision-making – were associated with patients who went on to have poorer outcomes. In contrast, in the second group, higher levels of grey matter signalled patients who were more likely to recover well from their illness.
A second algorithm was then used to predict the patients’ condition nine months following the initial diagnosis. The researchers found a higher level of accuracy in predicting outcomes when using the biologically based clusters than traditional diagnostic systems.
Evidence also showed that patients in the cluster with lower volumes of grey matter in their brain scans might have higher levels of inflammation, poorer concentration, and other cognitive impairments previously associated with depression and schizophrenia.
Finally, the team tested the clusters in other large cohort studies in Germany and the US and was able to show that the same identified clusters could be used to predict patient outcomes.
‘While the PRONIA study contained people recently diagnosed with their illness, the other datasets we used contained people with chronic conditions,’ explains Lalousis. ‘We found that the longer the duration of illness, the more likely a patient would fit into the first cluster with lower grey matter volume. That really adds to the evidence that structural MRI scans may be able to offer useful diagnostic information to help guide targeted treatment decisions.’
The next step for the team is to validate the clusters in the clinic, gathering patient data in real-time, before planning larger-scale clinical trials.
Muundo wa ubongo wa wagonjwa wenye ugonjwa wa akili na unyogovu wa hivi karibuni unaweza kutoa ufahamu muhimu wa kibiolojia juu ya magonjwa haya na jinsi yanaweza kuendeleza. Katika utafiti mpya uliochapishwa katika Biological Psychiatry, watafiti wa Chuo Kikuu cha Birmingham wanaonyesha kuwa inawezekana kutambua wagonjwa walio na uwezekano mkubwa wa matokeo mabaya kwa kuchunguza uchunguzi wa MRI ya muundo wa ubongo. Kwa kutambua wagonjwa hawa katika hatua za mapema za ugonjwa wao, madaktari wataweza kutoa matibabu yenye lengo na yenye ufanisi zaidi. "Kwa sasa, tunagundua matatizo mengi ya afya ya akili kwa kuzingatia historia ya mgonjwa, dalili, na uchunguzi wa kliniki, badala ya habari za kibiolojia", anasema mwandishi mkuu Paris Alexandros Lalousis. "Hilo lamaanisha kwamba wagonjwa wanaweza kuwa na utaratibu sawa wa kibiolojia katika ugonjwa wao lakini wagonjwa tofauti. "Kwa kuelewa utaratibu huo kwa undani zaidi, tunaweza kuwapa wataalamu wa matibabu zana bora za kutumia katika kupanga matibabu. ""Watafiti walitumia data kutoka kwa wagonjwa wapatao 300 wenye ugonjwa wa akili wa hivi karibuni na unyogovu wa hivi karibuni katika utafiti wa PRONIA." PRONIA ni utafiti wa jamii unaofadhiliwa na Umoja wa Ulaya unaochunguza zana za utabiri wa ugonjwa wa akili ambao unafanyika katika vituo saba vya utafiti wa Ulaya, pamoja na Birmingham. Watafiti walitumia algorithm ya kujifunza mashine kutathmini data kutoka kwa skani za ubongo wa wagonjwa na kuorodhesha hizi katika vikundi au makundi. Makundi mawili yalitambuliwa kulingana na uchunguzi huo, kila moja ikiwa na wagonjwa wenye ugonjwa wa akili na wagonjwa wenye mshuko wa moyo. Kila kundi lilionyesha sifa za kipekee ambazo zilihusiana sana na uwezekano wao wa kupona. Katika kundi la kwanza, kiasi cha chini cha nyenzo ya kijivu - tishu nyeusi ndani ya ubongo inayohusika katika udhibiti wa misuli na kazi kama vile kumbukumbu, hisia, na kufanya maamuzi - ilihusishwa na wagonjwa ambao waliendelea kuwa na matokeo mabaya zaidi. Kwa upande mwingine, katika kundi la pili, viwango vya juu vya nyenzo ya kijivu vilionyesha wagonjwa ambao walikuwa na uwezekano mkubwa wa kupona vizuri kutoka kwa ugonjwa wao. Algorithm ya pili ilitumiwa kutabiri hali ya wagonjwa miezi tisa baada ya utambuzi wa awali. Watafiti walipata kiwango cha juu cha usahihi katika kutabiri matokeo wakati wa kutumia makundi ya kibiolojia kuliko mifumo ya jadi ya utambuzi. Utafiti pia ulionyesha kuwa wagonjwa katika kundi la watu wenye kiasi kidogo cha nyenzo ya kijivu katika uchunguzi wa ubongo wao wanaweza kuwa na viwango vya juu vya uvimbe, mkusanyiko duni, na kasoro nyingine za utambuzi zilizohusishwa hapo awali na unyogovu na schizophrenia. Timu hiyo pia ilijaribu makundi hayo katika masomo mengine makubwa ya kikundi nchini Ujerumani na Marekani na kuweza kuonyesha kwamba makundi yale yale yaliyotambuliwa yanaweza kutumika kutabiri matokeo ya wagonjwa. "Wakati utafiti wa PRONIA ulijumuisha watu waliogunduliwa hivi karibuni na ugonjwa wao, seti nyingine za data tulizotumia zilijumuisha watu wenye hali ya muda mrefu, ""Lalousis anaelezea." """Tuliamua kuwa kadiri ugonjwa ulivyo na muda mrefu, ndivyo mgonjwa anavyoweza kuingizwa katika kundi la kwanza la watu wenye kiasi kidogo cha gray matter." "Hiyo inaongeza ushahidi kwamba uchunguzi wa MRI ya muundo unaweza kutoa habari muhimu ya utambuzi kusaidia kuongoza maamuzi ya matibabu yaliyolenga. ""Hatua inayofuata kwa timu ni kuthibitisha makundi katika kliniki, kukusanya data ya mgonjwa kwa wakati halisi, kabla ya kupanga majaribio makubwa ya kliniki."